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Nov 13 - Women in AI 2025-11-13 · 17:00 Hear talks from experts on the latest topics in AI, ML, and computer vision on November 13. Date and Location Nov 13, 2025 9 AM Pacific Online. Register for the Zoom! Copy, Paste, Customize! The Template Approach to AI Engineering Most AI implementations fail because teams treat prompt engineering as ad-hoc experimentation rather than systematic software engineering, leading to unreliable systems that don't scale beyond proof-of-concepts. This talk demonstrates engineering practices that enable reliable AI deployment through standardized prompt templates, systematic validation frameworks, and production observability. Drawing from experience developing fillable prompt templates currently being validated in production environments processing thousands of submissions, I'll share how Infrastructure as Code principles apply to LLM workflows, why evaluation metrics like BLEU scores are critical for production reliability, and how systematic failure analysis prevents costly deployment issues. Attendees will walk away with understanding of practical frameworks for improving AI system reliability and specific strategies for building more consistent, scalable AI implementations. About the Speaker Jeanne McClure is a postdoctoral scholar at NC State's Data Science and AI Academy with expertise in systematic AI implementation and validation. Her research transforms experimental AI tools into reliable production systems through standardized prompt templates, rigorous testing frameworks, and systematic failure analysis. She holds a PhD in Learning, Design and Technology with additional graduate work in data science. Multimodality with Biases: Understand and Evaluate VLMs for Autonomous Driving with FiftyOne Do your VLMs really see danger? With FiftyOne, I’ll show you how to understand and evaluate vision-language models for autonomous driving — making risk and bias visible in seconds. We’ll compare models on the same scenes, reveal failures and edge cases, and you’ll see a simple dashboard to decide which data to curate and what to adjust. You’ll leave with a clear, practical, and replicable method to raise the bar for safety. About the Speaker Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia. The Heart of Innovation: Women, AI, and the Future of Healthcare This session explores how Artificial Intelligence is transforming healthcare by enhancing diagnosis, treatment, and patient outcomes. It highlights the importance of diverse and female perspectives in shaping AI solutions that are ethical, empathetic, and human-centered. We will discuss key applications, current challenges, and the future potential of AI in medicine. It’s a forward-looking conversation about how innovation can build a healthier world. About the Speaker Karen Sanchez is a Postdoctoral Researcher at the Center of Excellence for Generative AI at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Her research focuses on AI for Science, spanning computer vision, video understanding, and privacy-preserving machine learning. She is also an active advocate for diversity and outreach in AI, contributing to global initiatives that connect researchers and amplify underrepresented voices in technology. Language Diffusion Models Autoregressive models (ARMs) are widely regarded as the cornerstone of large language models (LLMs). Challenge this notion by introducing LLaDA, a diffusion model trained from scratch under the pre-training and supervised fine-tuning (SFT) paradigm. LLaDA models distributions through a forward data masking process and a reverse process, parameterized by a vanilla Transformer to predict masked tokens. Optimizing a likelihood bound provides a principled generative approach for probabilistic inference. Across extensive benchmarks, LLaDA demonstrates strong scalability, outperforming self-constructed ARM baselines. Remarkably, LLaDA 8B is competitive with strong LLMs like LLaMA3 8B in in-context learning and, after SFT, exhibits impressive instruction-following abilities in case studies such as multi-turn dialogue. About the Speaker Jayita Bhattacharyya is an AI/ML Nerd with a blend of technical speaking & hackathon wizardry! Applying tech to solve real-world problems. The work focus these days is on generative AI. Helping software teams incorporate AI into transforming software engineering.	Nov 13 - Women in AI
Nov 13 - Women in AI 2025-11-13 · 17:00 Hear talks from experts on the latest topics in AI, ML, and computer vision on November 13. Date and Location Nov 13, 2025 9 AM Pacific Online. Register for the Zoom! Copy, Paste, Customize! The Template Approach to AI Engineering Most AI implementations fail because teams treat prompt engineering as ad-hoc experimentation rather than systematic software engineering, leading to unreliable systems that don't scale beyond proof-of-concepts. This talk demonstrates engineering practices that enable reliable AI deployment through standardized prompt templates, systematic validation frameworks, and production observability. Drawing from experience developing fillable prompt templates currently being validated in production environments processing thousands of submissions, I'll share how Infrastructure as Code principles apply to LLM workflows, why evaluation metrics like BLEU scores are critical for production reliability, and how systematic failure analysis prevents costly deployment issues. Attendees will walk away with understanding of practical frameworks for improving AI system reliability and specific strategies for building more consistent, scalable AI implementations. About the Speaker Jeanne McClure is a postdoctoral scholar at NC State's Data Science and AI Academy with expertise in systematic AI implementation and validation. Her research transforms experimental AI tools into reliable production systems through standardized prompt templates, rigorous testing frameworks, and systematic failure analysis. She holds a PhD in Learning, Design and Technology with additional graduate work in data science. Multimodality with Biases: Understand and Evaluate VLMs for Autonomous Driving with FiftyOne Do your VLMs really see danger? With FiftyOne, I’ll show you how to understand and evaluate vision-language models for autonomous driving — making risk and bias visible in seconds. We’ll compare models on the same scenes, reveal failures and edge cases, and you’ll see a simple dashboard to decide which data to curate and what to adjust. You’ll leave with a clear, practical, and replicable method to raise the bar for safety. About the Speaker Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia. The Heart of Innovation: Women, AI, and the Future of Healthcare This session explores how Artificial Intelligence is transforming healthcare by enhancing diagnosis, treatment, and patient outcomes. It highlights the importance of diverse and female perspectives in shaping AI solutions that are ethical, empathetic, and human-centered. We will discuss key applications, current challenges, and the future potential of AI in medicine. It’s a forward-looking conversation about how innovation can build a healthier world. About the Speaker Karen Sanchez is a Postdoctoral Researcher at the Center of Excellence for Generative AI at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Her research focuses on AI for Science, spanning computer vision, video understanding, and privacy-preserving machine learning. She is also an active advocate for diversity and outreach in AI, contributing to global initiatives that connect researchers and amplify underrepresented voices in technology. Language Diffusion Models Autoregressive models (ARMs) are widely regarded as the cornerstone of large language models (LLMs). Challenge this notion by introducing LLaDA, a diffusion model trained from scratch under the pre-training and supervised fine-tuning (SFT) paradigm. LLaDA models distributions through a forward data masking process and a reverse process, parameterized by a vanilla Transformer to predict masked tokens. Optimizing a likelihood bound provides a principled generative approach for probabilistic inference. Across extensive benchmarks, LLaDA demonstrates strong scalability, outperforming self-constructed ARM baselines. Remarkably, LLaDA 8B is competitive with strong LLMs like LLaMA3 8B in in-context learning and, after SFT, exhibits impressive instruction-following abilities in case studies such as multi-turn dialogue. About the Speaker Jayita Bhattacharyya is an AI/ML Nerd with a blend of technical speaking & hackathon wizardry! Applying tech to solve real-world problems. The work focus these days is on generative AI. Helping software teams incorporate AI into transforming software engineering.	Nov 13 - Women in AI
Nov 13 - Women in AI 2025-11-13 · 17:00 Hear talks from experts on the latest topics in AI, ML, and computer vision on November 13. Date and Location Nov 13, 2025 9 AM Pacific Online. Register for the Zoom! Copy, Paste, Customize! The Template Approach to AI Engineering Most AI implementations fail because teams treat prompt engineering as ad-hoc experimentation rather than systematic software engineering, leading to unreliable systems that don't scale beyond proof-of-concepts. This talk demonstrates engineering practices that enable reliable AI deployment through standardized prompt templates, systematic validation frameworks, and production observability. Drawing from experience developing fillable prompt templates currently being validated in production environments processing thousands of submissions, I'll share how Infrastructure as Code principles apply to LLM workflows, why evaluation metrics like BLEU scores are critical for production reliability, and how systematic failure analysis prevents costly deployment issues. Attendees will walk away with understanding of practical frameworks for improving AI system reliability and specific strategies for building more consistent, scalable AI implementations. About the Speaker Jeanne McClure is a postdoctoral scholar at NC State's Data Science and AI Academy with expertise in systematic AI implementation and validation. Her research transforms experimental AI tools into reliable production systems through standardized prompt templates, rigorous testing frameworks, and systematic failure analysis. She holds a PhD in Learning, Design and Technology with additional graduate work in data science. Multimodality with Biases: Understand and Evaluate VLMs for Autonomous Driving with FiftyOne Do your VLMs really see danger? With FiftyOne, I’ll show you how to understand and evaluate vision-language models for autonomous driving — making risk and bias visible in seconds. We’ll compare models on the same scenes, reveal failures and edge cases, and you’ll see a simple dashboard to decide which data to curate and what to adjust. You’ll leave with a clear, practical, and replicable method to raise the bar for safety. About the Speaker Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia. The Heart of Innovation: Women, AI, and the Future of Healthcare This session explores how Artificial Intelligence is transforming healthcare by enhancing diagnosis, treatment, and patient outcomes. It highlights the importance of diverse and female perspectives in shaping AI solutions that are ethical, empathetic, and human-centered. We will discuss key applications, current challenges, and the future potential of AI in medicine. It’s a forward-looking conversation about how innovation can build a healthier world. About the Speaker Karen Sanchez is a Postdoctoral Researcher at the Center of Excellence for Generative AI at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Her research focuses on AI for Science, spanning computer vision, video understanding, and privacy-preserving machine learning. She is also an active advocate for diversity and outreach in AI, contributing to global initiatives that connect researchers and amplify underrepresented voices in technology. Language Diffusion Models Autoregressive models (ARMs) are widely regarded as the cornerstone of large language models (LLMs). Challenge this notion by introducing LLaDA, a diffusion model trained from scratch under the pre-training and supervised fine-tuning (SFT) paradigm. LLaDA models distributions through a forward data masking process and a reverse process, parameterized by a vanilla Transformer to predict masked tokens. Optimizing a likelihood bound provides a principled generative approach for probabilistic inference. Across extensive benchmarks, LLaDA demonstrates strong scalability, outperforming self-constructed ARM baselines. Remarkably, LLaDA 8B is competitive with strong LLMs like LLaMA3 8B in in-context learning and, after SFT, exhibits impressive instruction-following abilities in case studies such as multi-turn dialogue. About the Speaker Jayita Bhattacharyya is an AI/ML Nerd with a blend of technical speaking & hackathon wizardry! Applying tech to solve real-world problems. The work focus these days is on generative AI. Helping software teams incorporate AI into transforming software engineering.	Nov 13 - Women in AI
Nov 13 - Women in AI 2025-11-13 · 17:00 Hear talks from experts on the latest topics in AI, ML, and computer vision on November 13. Date and Location Nov 13, 2025 9 AM Pacific Online. Register for the Zoom! Copy, Paste, Customize! The Template Approach to AI Engineering Most AI implementations fail because teams treat prompt engineering as ad-hoc experimentation rather than systematic software engineering, leading to unreliable systems that don't scale beyond proof-of-concepts. This talk demonstrates engineering practices that enable reliable AI deployment through standardized prompt templates, systematic validation frameworks, and production observability. Drawing from experience developing fillable prompt templates currently being validated in production environments processing thousands of submissions, I'll share how Infrastructure as Code principles apply to LLM workflows, why evaluation metrics like BLEU scores are critical for production reliability, and how systematic failure analysis prevents costly deployment issues. Attendees will walk away with understanding of practical frameworks for improving AI system reliability and specific strategies for building more consistent, scalable AI implementations. About the Speaker Jeanne McClure is a postdoctoral scholar at NC State's Data Science and AI Academy with expertise in systematic AI implementation and validation. Her research transforms experimental AI tools into reliable production systems through standardized prompt templates, rigorous testing frameworks, and systematic failure analysis. She holds a PhD in Learning, Design and Technology with additional graduate work in data science. Multimodality with Biases: Understand and Evaluate VLMs for Autonomous Driving with FiftyOne Do your VLMs really see danger? With FiftyOne, I’ll show you how to understand and evaluate vision-language models for autonomous driving — making risk and bias visible in seconds. We’ll compare models on the same scenes, reveal failures and edge cases, and you’ll see a simple dashboard to decide which data to curate and what to adjust. You’ll leave with a clear, practical, and replicable method to raise the bar for safety. About the Speaker Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia. The Heart of Innovation: Women, AI, and the Future of Healthcare This session explores how Artificial Intelligence is transforming healthcare by enhancing diagnosis, treatment, and patient outcomes. It highlights the importance of diverse and female perspectives in shaping AI solutions that are ethical, empathetic, and human-centered. We will discuss key applications, current challenges, and the future potential of AI in medicine. It’s a forward-looking conversation about how innovation can build a healthier world. About the Speaker Karen Sanchez is a Postdoctoral Researcher at the Center of Excellence for Generative AI at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Her research focuses on AI for Science, spanning computer vision, video understanding, and privacy-preserving machine learning. She is also an active advocate for diversity and outreach in AI, contributing to global initiatives that connect researchers and amplify underrepresented voices in technology. Language Diffusion Models Autoregressive models (ARMs) are widely regarded as the cornerstone of large language models (LLMs). Challenge this notion by introducing LLaDA, a diffusion model trained from scratch under the pre-training and supervised fine-tuning (SFT) paradigm. LLaDA models distributions through a forward data masking process and a reverse process, parameterized by a vanilla Transformer to predict masked tokens. Optimizing a likelihood bound provides a principled generative approach for probabilistic inference. Across extensive benchmarks, LLaDA demonstrates strong scalability, outperforming self-constructed ARM baselines. Remarkably, LLaDA 8B is competitive with strong LLMs like LLaMA3 8B in in-context learning and, after SFT, exhibits impressive instruction-following abilities in case studies such as multi-turn dialogue. About the Speaker Jayita Bhattacharyya is an AI/ML Nerd with a blend of technical speaking & hackathon wizardry! Applying tech to solve real-world problems. The work focus these days is on generative AI. Helping software teams incorporate AI into transforming software engineering.	Nov 13 - Women in AI
Nov 13 - Women in AI 2025-11-13 · 17:00 Hear talks from experts on the latest topics in AI, ML, and computer vision on November 13. Date and Location Nov 13, 2025 9 AM Pacific Online. Register for the Zoom! Copy, Paste, Customize! The Template Approach to AI Engineering Most AI implementations fail because teams treat prompt engineering as ad-hoc experimentation rather than systematic software engineering, leading to unreliable systems that don't scale beyond proof-of-concepts. This talk demonstrates engineering practices that enable reliable AI deployment through standardized prompt templates, systematic validation frameworks, and production observability. Drawing from experience developing fillable prompt templates currently being validated in production environments processing thousands of submissions, I'll share how Infrastructure as Code principles apply to LLM workflows, why evaluation metrics like BLEU scores are critical for production reliability, and how systematic failure analysis prevents costly deployment issues. Attendees will walk away with understanding of practical frameworks for improving AI system reliability and specific strategies for building more consistent, scalable AI implementations. About the Speaker Jeanne McClure is a postdoctoral scholar at NC State's Data Science and AI Academy with expertise in systematic AI implementation and validation. Her research transforms experimental AI tools into reliable production systems through standardized prompt templates, rigorous testing frameworks, and systematic failure analysis. She holds a PhD in Learning, Design and Technology with additional graduate work in data science. Multimodality with Biases: Understand and Evaluate VLMs for Autonomous Driving with FiftyOne Do your VLMs really see danger? With FiftyOne, I’ll show you how to understand and evaluate vision-language models for autonomous driving — making risk and bias visible in seconds. We’ll compare models on the same scenes, reveal failures and edge cases, and you’ll see a simple dashboard to decide which data to curate and what to adjust. You’ll leave with a clear, practical, and replicable method to raise the bar for safety. About the Speaker Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia. The Heart of Innovation: Women, AI, and the Future of Healthcare This session explores how Artificial Intelligence is transforming healthcare by enhancing diagnosis, treatment, and patient outcomes. It highlights the importance of diverse and female perspectives in shaping AI solutions that are ethical, empathetic, and human-centered. We will discuss key applications, current challenges, and the future potential of AI in medicine. It’s a forward-looking conversation about how innovation can build a healthier world. About the Speaker Karen Sanchez is a Postdoctoral Researcher at the Center of Excellence for Generative AI at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Her research focuses on AI for Science, spanning computer vision, video understanding, and privacy-preserving machine learning. She is also an active advocate for diversity and outreach in AI, contributing to global initiatives that connect researchers and amplify underrepresented voices in technology. Language Diffusion Models Autoregressive models (ARMs) are widely regarded as the cornerstone of large language models (LLMs). Challenge this notion by introducing LLaDA, a diffusion model trained from scratch under the pre-training and supervised fine-tuning (SFT) paradigm. LLaDA models distributions through a forward data masking process and a reverse process, parameterized by a vanilla Transformer to predict masked tokens. Optimizing a likelihood bound provides a principled generative approach for probabilistic inference. Across extensive benchmarks, LLaDA demonstrates strong scalability, outperforming self-constructed ARM baselines. Remarkably, LLaDA 8B is competitive with strong LLMs like LLaMA3 8B in in-context learning and, after SFT, exhibits impressive instruction-following abilities in case studies such as multi-turn dialogue. About the Speaker Jayita Bhattacharyya is an AI/ML Nerd with a blend of technical speaking & hackathon wizardry! Applying tech to solve real-world problems. The work focus these days is on generative AI. Helping software teams incorporate AI into transforming software engineering.	Nov 13 - Women in AI
Nov 13 - Women in AI 2025-11-13 · 17:00 Hear talks from experts on the latest topics in AI, ML, and computer vision on November 13. Date and Location Nov 13, 2025 9 AM Pacific Online. Register for the Zoom! Copy, Paste, Customize! The Template Approach to AI Engineering Most AI implementations fail because teams treat prompt engineering as ad-hoc experimentation rather than systematic software engineering, leading to unreliable systems that don't scale beyond proof-of-concepts. This talk demonstrates engineering practices that enable reliable AI deployment through standardized prompt templates, systematic validation frameworks, and production observability. Drawing from experience developing fillable prompt templates currently being validated in production environments processing thousands of submissions, I'll share how Infrastructure as Code principles apply to LLM workflows, why evaluation metrics like BLEU scores are critical for production reliability, and how systematic failure analysis prevents costly deployment issues. Attendees will walk away with understanding of practical frameworks for improving AI system reliability and specific strategies for building more consistent, scalable AI implementations. About the Speaker Jeanne McClure is a postdoctoral scholar at NC State's Data Science and AI Academy with expertise in systematic AI implementation and validation. Her research transforms experimental AI tools into reliable production systems through standardized prompt templates, rigorous testing frameworks, and systematic failure analysis. She holds a PhD in Learning, Design and Technology with additional graduate work in data science. Multimodality with Biases: Understand and Evaluate VLMs for Autonomous Driving with FiftyOne Do your VLMs really see danger? With FiftyOne, I’ll show you how to understand and evaluate vision-language models for autonomous driving — making risk and bias visible in seconds. We’ll compare models on the same scenes, reveal failures and edge cases, and you’ll see a simple dashboard to decide which data to curate and what to adjust. You’ll leave with a clear, practical, and replicable method to raise the bar for safety. About the Speaker Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia. The Heart of Innovation: Women, AI, and the Future of Healthcare This session explores how Artificial Intelligence is transforming healthcare by enhancing diagnosis, treatment, and patient outcomes. It highlights the importance of diverse and female perspectives in shaping AI solutions that are ethical, empathetic, and human-centered. We will discuss key applications, current challenges, and the future potential of AI in medicine. It’s a forward-looking conversation about how innovation can build a healthier world. About the Speaker Karen Sanchez is a Postdoctoral Researcher at the Center of Excellence for Generative AI at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Her research focuses on AI for Science, spanning computer vision, video understanding, and privacy-preserving machine learning. She is also an active advocate for diversity and outreach in AI, contributing to global initiatives that connect researchers and amplify underrepresented voices in technology. Language Diffusion Models Autoregressive models (ARMs) are widely regarded as the cornerstone of large language models (LLMs). Challenge this notion by introducing LLaDA, a diffusion model trained from scratch under the pre-training and supervised fine-tuning (SFT) paradigm. LLaDA models distributions through a forward data masking process and a reverse process, parameterized by a vanilla Transformer to predict masked tokens. Optimizing a likelihood bound provides a principled generative approach for probabilistic inference. Across extensive benchmarks, LLaDA demonstrates strong scalability, outperforming self-constructed ARM baselines. Remarkably, LLaDA 8B is competitive with strong LLMs like LLaMA3 8B in in-context learning and, after SFT, exhibits impressive instruction-following abilities in case studies such as multi-turn dialogue. About the Speaker Jayita Bhattacharyya is an AI/ML Nerd with a blend of technical speaking & hackathon wizardry! Applying tech to solve real-world problems. The work focus these days is on generative AI. Helping software teams incorporate AI into transforming software engineering.	Nov 13 - Women in AI
Nov 13 - Women in AI 2025-11-13 · 17:00 Hear talks from experts on the latest topics in AI, ML, and computer vision on November 13. Date and Location Nov 13, 2025 9 AM Pacific Online. Register for the Zoom! Copy, Paste, Customize! The Template Approach to AI Engineering Most AI implementations fail because teams treat prompt engineering as ad-hoc experimentation rather than systematic software engineering, leading to unreliable systems that don't scale beyond proof-of-concepts. This talk demonstrates engineering practices that enable reliable AI deployment through standardized prompt templates, systematic validation frameworks, and production observability. Drawing from experience developing fillable prompt templates currently being validated in production environments processing thousands of submissions, I'll share how Infrastructure as Code principles apply to LLM workflows, why evaluation metrics like BLEU scores are critical for production reliability, and how systematic failure analysis prevents costly deployment issues. Attendees will walk away with understanding of practical frameworks for improving AI system reliability and specific strategies for building more consistent, scalable AI implementations. About the Speaker Jeanne McClure is a postdoctoral scholar at NC State's Data Science and AI Academy with expertise in systematic AI implementation and validation. Her research transforms experimental AI tools into reliable production systems through standardized prompt templates, rigorous testing frameworks, and systematic failure analysis. She holds a PhD in Learning, Design and Technology with additional graduate work in data science. Multimodality with Biases: Understand and Evaluate VLMs for Autonomous Driving with FiftyOne Do your VLMs really see danger? With FiftyOne, I’ll show you how to understand and evaluate vision-language models for autonomous driving — making risk and bias visible in seconds. We’ll compare models on the same scenes, reveal failures and edge cases, and you’ll see a simple dashboard to decide which data to curate and what to adjust. You’ll leave with a clear, practical, and replicable method to raise the bar for safety. About the Speaker Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia. The Heart of Innovation: Women, AI, and the Future of Healthcare This session explores how Artificial Intelligence is transforming healthcare by enhancing diagnosis, treatment, and patient outcomes. It highlights the importance of diverse and female perspectives in shaping AI solutions that are ethical, empathetic, and human-centered. We will discuss key applications, current challenges, and the future potential of AI in medicine. It’s a forward-looking conversation about how innovation can build a healthier world. About the Speaker Karen Sanchez is a Postdoctoral Researcher at the Center of Excellence for Generative AI at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Her research focuses on AI for Science, spanning computer vision, video understanding, and privacy-preserving machine learning. She is also an active advocate for diversity and outreach in AI, contributing to global initiatives that connect researchers and amplify underrepresented voices in technology. Language Diffusion Models Autoregressive models (ARMs) are widely regarded as the cornerstone of large language models (LLMs). Challenge this notion by introducing LLaDA, a diffusion model trained from scratch under the pre-training and supervised fine-tuning (SFT) paradigm. LLaDA models distributions through a forward data masking process and a reverse process, parameterized by a vanilla Transformer to predict masked tokens. Optimizing a likelihood bound provides a principled generative approach for probabilistic inference. Across extensive benchmarks, LLaDA demonstrates strong scalability, outperforming self-constructed ARM baselines. Remarkably, LLaDA 8B is competitive with strong LLMs like LLaMA3 8B in in-context learning and, after SFT, exhibits impressive instruction-following abilities in case studies such as multi-turn dialogue. About the Speaker Jayita Bhattacharyya is an AI/ML Nerd with a blend of technical speaking & hackathon wizardry! Applying tech to solve real-world problems. The work focus these days is on generative AI. Helping software teams incorporate AI into transforming software engineering.	Nov 13 - Women in AI
Oct 15 - Visual AI in Agriculture (Day 1) 2025-10-15 · 16:00 Join us for day one of a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI in Agriculture. Date and Time Oct 15 at 9 AM Pacific Location Virtual. Register for the Zoom. Paved2Paradise: Scalable LiDAR Simulation for Real-World Perception Training robust perception models for robotics and autonomy often requires massive, diverse 3D datasets. But collecting and annotating real-world LiDAR point clouds at scale is both expensive and time-consuming, especially when high-quality labels are needed. Paved2Paradise introduces a cost-effective alternative: a scalable LiDAR simulation pipeline that generates realistic, fully annotated datasets with minimal human labeling effort. The key idea is to “factor the real world” by separately capturing background scans (e.g., fields, roads, construction sites) and object scans (e.g., vehicles, people, machinery). By intelligently combining these two sources, Paved2Paradise can synthesize a combinatorially large set of diverse training scenes. The pipeline involves four steps: (1) collecting extensive background LiDAR scans, (2) recording high-resolution scans of target objects under controlled conditions, (3) inserting objects into backgrounds with physically consistent placement and occlusion, and (4) simulating LiDAR geometry to ensure realism. Experiments show that models trained on Paved2Paradise-generated data transfer effectively to the real world, achieving strong detection performance with far less manual annotation compared to conventional dataset collection. The approach is not only cost-efficient, but also flexible—allowing practitioners to easily expand to new object classes or domains by swapping in new background or object scans. For ML practitioners working in robotics, autonomous vehicles, or safety-critical perception, Paved2Paradise highlights a practical path toward scaling training data without scaling costs. It bridges the gap between simulation and real-world performance, enabling faster iteration and more reliable deployment of perception models. About the Speaker Michael A. Alcorn is a Senior Machine Learning Engineer at John Deere\, where he develops deep learning models for LiDAR and RGB perception in safety-critical\, real-time systems. He earned his Ph.D. in Computer Science from Auburn University\, with a dissertation on improving computer vision and spatiotemporal deep neural networks\, and also holds a Graduate Minor in Mathematics. Michael’s research has been cited by researchers at DeepMind\, Google\, Meta\, Microsoft\, and OpenAI\, among others\, and his (batter\\|pitcher)2vec paper was a prize-winner at the 2018 MIT Sloan Sports Analytics Conference. He has also contributed machine learning code to scikit-learn and Apache Solr\, and his GitHub repositories—which have collectively received over 2\,100 stars—have served as starting points for research and production code at many different organizations. MothBox: inexpensive, open-source, automated insect monitor Dr. Andy Quitmeyer will talk about the design of an exciting new open source science tool, The Mothbox. The Mothbox is an award winning project for broad scale monitoring of insects for biodiversity. It's a low cost device developed in harsh Panamanian jungles which takes super high resolution photos to then automatically ID the levels of biodiversity in forests and agriculture. After thousands of insect observations and hundreds of deployments in Panama, Peru, Mexico, Ecuador, and the US, we are now developing a new, manufacturable version to share this important tool worldwide. We will discuss the development of this device in the jungles of Panama and its importance to studying biodiversity worldwide. About the Speaker Dr. Andy Quitmeyer designs new ways to interact with the natural world. He has worked with large organizations like Cartoon Network, IDEO, and the Smithsonian, taught as a tenure-track professor at the National University of Singapore, and even had his research turned into a (silly) television series called “Hacking the Wild,” distributed by Discovery Networks. Now, he spends most of his time volunteering with smaller organizations, and recently founded the field-station makerspace, Digital Naturalism Laboratories. In the rainforest of Gamboa, Panama, Dinalab blends biological fieldwork and technological crafting with a community of local and international scientists, artists, engineers, and animal rehabilitators. He currently also advises students as an affiliate professor at the University of Washington. Foundation Models for Visual AI in Agriculture Foundation models have enabled a new way to address tasks, by benefitting from emerging capabilities in a zero-shot manner. In this talk I will discuss recent research on enabling visual AI in a zero-shot manner and via fine-tuning. Specifically, I will discuss joint work on RELOCATE, a simple training-free baseline designed to perform the challenging task of visual query localization in long videos. To eliminate the need for task-specific training and efficiently handle long videos, RELOCATE leverages a region-based representation derived from pretrained vision models. I will also discuss joint work on enabling multi-modal large language models (MLLMs) to correctly answer prompts that require a holistic spatio-temporal understanding: MLLMs struggle to answer prompts that refer to 1) the entirety of an environment that an agent equipped with an MLLM can operate in; and simultaneously also refer to 2) recent actions that just happened and are encoded in a video clip. However, such a holistic spatio-temporal understanding is important for agents operating in the real world. Our solution involves development of a dedicated data collection pipeline and fine-tuning of an MLLM equipped with projectors to improve both spatial understanding of an environment and temporal understanding of recent observations. About the Speaker Alex Schwing is an Associate Professor at the University of Illinois at Urbana-Champaign working with talented students on artificial intelligence, generative AI, and computer vision topics. He received his B.S. and diploma in Electrical Engineering and Information Technology from the Technical University of Munich in 2006 and 2008 respectively, and obtained a PhD in Computer Science from ETH Zurich in 2014. Afterwards he joined University of Toronto as a postdoctoral fellow until 2016. His research interests are in the area of artificial intelligence, generative AI, and computer vision, where he has co-authored numerous papers on topics in scene understanding, inference and learning algorithms, deep learning, image and language processing, and generative modeling. His PhD thesis was awarded an ETH medal and his team’s research was awarded an NSF CAREER award. Beyond the Lab: Real-World Anomaly Detection for Agricultural Computer Vision Anomaly detection is transforming manufacturing and surveillance, but what about agriculture? Can AI actually detect plant diseases and pest damage early enough to make a difference? This talk demonstrates how anomaly detection identifies and localizes crop problems using coffee leaf health as our primary example. We'll start with the foundational theory, then examine how these models detect rust and miner damage in leaf imagery. The session includes a comprehensive hands-on workflow using the open-source FiftyOne computer vision toolkit, covering dataset curation, patch extraction, model training, and result visualization. You'll gain both theoretical understanding of anomaly detection in computer vision and practical experience applying these techniques to agricultural challenges and other domains. About the Speaker Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.	Oct 15 - Visual AI in Agriculture (Day 1)
Oct 15 - Visual AI in Agriculture (Day 1) 2025-10-15 · 16:00 Join us for day one of a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI in Agriculture. Date and Time Oct 15 at 9 AM Pacific Location Virtual. Register for the Zoom. Paved2Paradise: Scalable LiDAR Simulation for Real-World Perception Training robust perception models for robotics and autonomy often requires massive, diverse 3D datasets. But collecting and annotating real-world LiDAR point clouds at scale is both expensive and time-consuming, especially when high-quality labels are needed. Paved2Paradise introduces a cost-effective alternative: a scalable LiDAR simulation pipeline that generates realistic, fully annotated datasets with minimal human labeling effort. The key idea is to “factor the real world” by separately capturing background scans (e.g., fields, roads, construction sites) and object scans (e.g., vehicles, people, machinery). By intelligently combining these two sources, Paved2Paradise can synthesize a combinatorially large set of diverse training scenes. The pipeline involves four steps: (1) collecting extensive background LiDAR scans, (2) recording high-resolution scans of target objects under controlled conditions, (3) inserting objects into backgrounds with physically consistent placement and occlusion, and (4) simulating LiDAR geometry to ensure realism. Experiments show that models trained on Paved2Paradise-generated data transfer effectively to the real world, achieving strong detection performance with far less manual annotation compared to conventional dataset collection. The approach is not only cost-efficient, but also flexible—allowing practitioners to easily expand to new object classes or domains by swapping in new background or object scans. For ML practitioners working in robotics, autonomous vehicles, or safety-critical perception, Paved2Paradise highlights a practical path toward scaling training data without scaling costs. It bridges the gap between simulation and real-world performance, enabling faster iteration and more reliable deployment of perception models. About the Speaker Michael A. Alcorn is a Senior Machine Learning Engineer at John Deere\, where he develops deep learning models for LiDAR and RGB perception in safety-critical\, real-time systems. He earned his Ph.D. in Computer Science from Auburn University\, with a dissertation on improving computer vision and spatiotemporal deep neural networks\, and also holds a Graduate Minor in Mathematics. Michael’s research has been cited by researchers at DeepMind\, Google\, Meta\, Microsoft\, and OpenAI\, among others\, and his (batter\\|pitcher)2vec paper was a prize-winner at the 2018 MIT Sloan Sports Analytics Conference. He has also contributed machine learning code to scikit-learn and Apache Solr\, and his GitHub repositories—which have collectively received over 2\,100 stars—have served as starting points for research and production code at many different organizations. MothBox: inexpensive, open-source, automated insect monitor Dr. Andy Quitmeyer will talk about the design of an exciting new open source science tool, The Mothbox. The Mothbox is an award winning project for broad scale monitoring of insects for biodiversity. It's a low cost device developed in harsh Panamanian jungles which takes super high resolution photos to then automatically ID the levels of biodiversity in forests and agriculture. After thousands of insect observations and hundreds of deployments in Panama, Peru, Mexico, Ecuador, and the US, we are now developing a new, manufacturable version to share this important tool worldwide. We will discuss the development of this device in the jungles of Panama and its importance to studying biodiversity worldwide. About the Speaker Dr. Andy Quitmeyer designs new ways to interact with the natural world. He has worked with large organizations like Cartoon Network, IDEO, and the Smithsonian, taught as a tenure-track professor at the National University of Singapore, and even had his research turned into a (silly) television series called “Hacking the Wild,” distributed by Discovery Networks. Now, he spends most of his time volunteering with smaller organizations, and recently founded the field-station makerspace, Digital Naturalism Laboratories. In the rainforest of Gamboa, Panama, Dinalab blends biological fieldwork and technological crafting with a community of local and international scientists, artists, engineers, and animal rehabilitators. He currently also advises students as an affiliate professor at the University of Washington. Foundation Models for Visual AI in Agriculture Foundation models have enabled a new way to address tasks, by benefitting from emerging capabilities in a zero-shot manner. In this talk I will discuss recent research on enabling visual AI in a zero-shot manner and via fine-tuning. Specifically, I will discuss joint work on RELOCATE, a simple training-free baseline designed to perform the challenging task of visual query localization in long videos. To eliminate the need for task-specific training and efficiently handle long videos, RELOCATE leverages a region-based representation derived from pretrained vision models. I will also discuss joint work on enabling multi-modal large language models (MLLMs) to correctly answer prompts that require a holistic spatio-temporal understanding: MLLMs struggle to answer prompts that refer to 1) the entirety of an environment that an agent equipped with an MLLM can operate in; and simultaneously also refer to 2) recent actions that just happened and are encoded in a video clip. However, such a holistic spatio-temporal understanding is important for agents operating in the real world. Our solution involves development of a dedicated data collection pipeline and fine-tuning of an MLLM equipped with projectors to improve both spatial understanding of an environment and temporal understanding of recent observations. About the Speaker Alex Schwing is an Associate Professor at the University of Illinois at Urbana-Champaign working with talented students on artificial intelligence, generative AI, and computer vision topics. He received his B.S. and diploma in Electrical Engineering and Information Technology from the Technical University of Munich in 2006 and 2008 respectively, and obtained a PhD in Computer Science from ETH Zurich in 2014. Afterwards he joined University of Toronto as a postdoctoral fellow until 2016. His research interests are in the area of artificial intelligence, generative AI, and computer vision, where he has co-authored numerous papers on topics in scene understanding, inference and learning algorithms, deep learning, image and language processing, and generative modeling. His PhD thesis was awarded an ETH medal and his team’s research was awarded an NSF CAREER award. Beyond the Lab: Real-World Anomaly Detection for Agricultural Computer Vision Anomaly detection is transforming manufacturing and surveillance, but what about agriculture? Can AI actually detect plant diseases and pest damage early enough to make a difference? This talk demonstrates how anomaly detection identifies and localizes crop problems using coffee leaf health as our primary example. We'll start with the foundational theory, then examine how these models detect rust and miner damage in leaf imagery. The session includes a comprehensive hands-on workflow using the open-source FiftyOne computer vision toolkit, covering dataset curation, patch extraction, model training, and result visualization. You'll gain both theoretical understanding of anomaly detection in computer vision and practical experience applying these techniques to agricultural challenges and other domains. About the Speaker Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.	Oct 15 - Visual AI in Agriculture (Day 1)
Oct 15 - Visual AI in Agriculture (Day 1) 2025-10-15 · 16:00 Join us for day one of a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI in Agriculture. Date and Time Oct 15 at 9 AM Pacific Location Virtual. Register for the Zoom. Paved2Paradise: Scalable LiDAR Simulation for Real-World Perception Training robust perception models for robotics and autonomy often requires massive, diverse 3D datasets. But collecting and annotating real-world LiDAR point clouds at scale is both expensive and time-consuming, especially when high-quality labels are needed. Paved2Paradise introduces a cost-effective alternative: a scalable LiDAR simulation pipeline that generates realistic, fully annotated datasets with minimal human labeling effort. The key idea is to “factor the real world” by separately capturing background scans (e.g., fields, roads, construction sites) and object scans (e.g., vehicles, people, machinery). By intelligently combining these two sources, Paved2Paradise can synthesize a combinatorially large set of diverse training scenes. The pipeline involves four steps: (1) collecting extensive background LiDAR scans, (2) recording high-resolution scans of target objects under controlled conditions, (3) inserting objects into backgrounds with physically consistent placement and occlusion, and (4) simulating LiDAR geometry to ensure realism. Experiments show that models trained on Paved2Paradise-generated data transfer effectively to the real world, achieving strong detection performance with far less manual annotation compared to conventional dataset collection. The approach is not only cost-efficient, but also flexible—allowing practitioners to easily expand to new object classes or domains by swapping in new background or object scans. For ML practitioners working in robotics, autonomous vehicles, or safety-critical perception, Paved2Paradise highlights a practical path toward scaling training data without scaling costs. It bridges the gap between simulation and real-world performance, enabling faster iteration and more reliable deployment of perception models. About the Speaker Michael A. Alcorn is a Senior Machine Learning Engineer at John Deere\, where he develops deep learning models for LiDAR and RGB perception in safety-critical\, real-time systems. He earned his Ph.D. in Computer Science from Auburn University\, with a dissertation on improving computer vision and spatiotemporal deep neural networks\, and also holds a Graduate Minor in Mathematics. Michael’s research has been cited by researchers at DeepMind\, Google\, Meta\, Microsoft\, and OpenAI\, among others\, and his (batter\\|pitcher)2vec paper was a prize-winner at the 2018 MIT Sloan Sports Analytics Conference. He has also contributed machine learning code to scikit-learn and Apache Solr\, and his GitHub repositories—which have collectively received over 2\,100 stars—have served as starting points for research and production code at many different organizations. MothBox: inexpensive, open-source, automated insect monitor Dr. Andy Quitmeyer will talk about the design of an exciting new open source science tool, The Mothbox. The Mothbox is an award winning project for broad scale monitoring of insects for biodiversity. It's a low cost device developed in harsh Panamanian jungles which takes super high resolution photos to then automatically ID the levels of biodiversity in forests and agriculture. After thousands of insect observations and hundreds of deployments in Panama, Peru, Mexico, Ecuador, and the US, we are now developing a new, manufacturable version to share this important tool worldwide. We will discuss the development of this device in the jungles of Panama and its importance to studying biodiversity worldwide. About the Speaker Dr. Andy Quitmeyer designs new ways to interact with the natural world. He has worked with large organizations like Cartoon Network, IDEO, and the Smithsonian, taught as a tenure-track professor at the National University of Singapore, and even had his research turned into a (silly) television series called “Hacking the Wild,” distributed by Discovery Networks. Now, he spends most of his time volunteering with smaller organizations, and recently founded the field-station makerspace, Digital Naturalism Laboratories. In the rainforest of Gamboa, Panama, Dinalab blends biological fieldwork and technological crafting with a community of local and international scientists, artists, engineers, and animal rehabilitators. He currently also advises students as an affiliate professor at the University of Washington. Foundation Models for Visual AI in Agriculture Foundation models have enabled a new way to address tasks, by benefitting from emerging capabilities in a zero-shot manner. In this talk I will discuss recent research on enabling visual AI in a zero-shot manner and via fine-tuning. Specifically, I will discuss joint work on RELOCATE, a simple training-free baseline designed to perform the challenging task of visual query localization in long videos. To eliminate the need for task-specific training and efficiently handle long videos, RELOCATE leverages a region-based representation derived from pretrained vision models. I will also discuss joint work on enabling multi-modal large language models (MLLMs) to correctly answer prompts that require a holistic spatio-temporal understanding: MLLMs struggle to answer prompts that refer to 1) the entirety of an environment that an agent equipped with an MLLM can operate in; and simultaneously also refer to 2) recent actions that just happened and are encoded in a video clip. However, such a holistic spatio-temporal understanding is important for agents operating in the real world. Our solution involves development of a dedicated data collection pipeline and fine-tuning of an MLLM equipped with projectors to improve both spatial understanding of an environment and temporal understanding of recent observations. About the Speaker Alex Schwing is an Associate Professor at the University of Illinois at Urbana-Champaign working with talented students on artificial intelligence, generative AI, and computer vision topics. He received his B.S. and diploma in Electrical Engineering and Information Technology from the Technical University of Munich in 2006 and 2008 respectively, and obtained a PhD in Computer Science from ETH Zurich in 2014. Afterwards he joined University of Toronto as a postdoctoral fellow until 2016. His research interests are in the area of artificial intelligence, generative AI, and computer vision, where he has co-authored numerous papers on topics in scene understanding, inference and learning algorithms, deep learning, image and language processing, and generative modeling. His PhD thesis was awarded an ETH medal and his team’s research was awarded an NSF CAREER award. Beyond the Lab: Real-World Anomaly Detection for Agricultural Computer Vision Anomaly detection is transforming manufacturing and surveillance, but what about agriculture? Can AI actually detect plant diseases and pest damage early enough to make a difference? This talk demonstrates how anomaly detection identifies and localizes crop problems using coffee leaf health as our primary example. We'll start with the foundational theory, then examine how these models detect rust and miner damage in leaf imagery. The session includes a comprehensive hands-on workflow using the open-source FiftyOne computer vision toolkit, covering dataset curation, patch extraction, model training, and result visualization. You'll gain both theoretical understanding of anomaly detection in computer vision and practical experience applying these techniques to agricultural challenges and other domains. About the Speaker Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.	Oct 15 - Visual AI in Agriculture (Day 1)
Oct 15 - Visual AI in Agriculture (Day 1) 2025-10-15 · 16:00 Join us for day one of a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI in Agriculture. Date and Time Oct 15 at 9 AM Pacific Location Virtual. Register for the Zoom. Paved2Paradise: Scalable LiDAR Simulation for Real-World Perception Training robust perception models for robotics and autonomy often requires massive, diverse 3D datasets. But collecting and annotating real-world LiDAR point clouds at scale is both expensive and time-consuming, especially when high-quality labels are needed. Paved2Paradise introduces a cost-effective alternative: a scalable LiDAR simulation pipeline that generates realistic, fully annotated datasets with minimal human labeling effort. The key idea is to “factor the real world” by separately capturing background scans (e.g., fields, roads, construction sites) and object scans (e.g., vehicles, people, machinery). By intelligently combining these two sources, Paved2Paradise can synthesize a combinatorially large set of diverse training scenes. The pipeline involves four steps: (1) collecting extensive background LiDAR scans, (2) recording high-resolution scans of target objects under controlled conditions, (3) inserting objects into backgrounds with physically consistent placement and occlusion, and (4) simulating LiDAR geometry to ensure realism. Experiments show that models trained on Paved2Paradise-generated data transfer effectively to the real world, achieving strong detection performance with far less manual annotation compared to conventional dataset collection. The approach is not only cost-efficient, but also flexible—allowing practitioners to easily expand to new object classes or domains by swapping in new background or object scans. For ML practitioners working in robotics, autonomous vehicles, or safety-critical perception, Paved2Paradise highlights a practical path toward scaling training data without scaling costs. It bridges the gap between simulation and real-world performance, enabling faster iteration and more reliable deployment of perception models. About the Speaker Michael A. Alcorn is a Senior Machine Learning Engineer at John Deere\, where he develops deep learning models for LiDAR and RGB perception in safety-critical\, real-time systems. He earned his Ph.D. in Computer Science from Auburn University\, with a dissertation on improving computer vision and spatiotemporal deep neural networks\, and also holds a Graduate Minor in Mathematics. Michael’s research has been cited by researchers at DeepMind\, Google\, Meta\, Microsoft\, and OpenAI\, among others\, and his (batter\\|pitcher)2vec paper was a prize-winner at the 2018 MIT Sloan Sports Analytics Conference. He has also contributed machine learning code to scikit-learn and Apache Solr\, and his GitHub repositories—which have collectively received over 2\,100 stars—have served as starting points for research and production code at many different organizations. MothBox: inexpensive, open-source, automated insect monitor Dr. Andy Quitmeyer will talk about the design of an exciting new open source science tool, The Mothbox. The Mothbox is an award winning project for broad scale monitoring of insects for biodiversity. It's a low cost device developed in harsh Panamanian jungles which takes super high resolution photos to then automatically ID the levels of biodiversity in forests and agriculture. After thousands of insect observations and hundreds of deployments in Panama, Peru, Mexico, Ecuador, and the US, we are now developing a new, manufacturable version to share this important tool worldwide. We will discuss the development of this device in the jungles of Panama and its importance to studying biodiversity worldwide. About the Speaker Dr. Andy Quitmeyer designs new ways to interact with the natural world. He has worked with large organizations like Cartoon Network, IDEO, and the Smithsonian, taught as a tenure-track professor at the National University of Singapore, and even had his research turned into a (silly) television series called “Hacking the Wild,” distributed by Discovery Networks. Now, he spends most of his time volunteering with smaller organizations, and recently founded the field-station makerspace, Digital Naturalism Laboratories. In the rainforest of Gamboa, Panama, Dinalab blends biological fieldwork and technological crafting with a community of local and international scientists, artists, engineers, and animal rehabilitators. He currently also advises students as an affiliate professor at the University of Washington. Foundation Models for Visual AI in Agriculture Foundation models have enabled a new way to address tasks, by benefitting from emerging capabilities in a zero-shot manner. In this talk I will discuss recent research on enabling visual AI in a zero-shot manner and via fine-tuning. Specifically, I will discuss joint work on RELOCATE, a simple training-free baseline designed to perform the challenging task of visual query localization in long videos. To eliminate the need for task-specific training and efficiently handle long videos, RELOCATE leverages a region-based representation derived from pretrained vision models. I will also discuss joint work on enabling multi-modal large language models (MLLMs) to correctly answer prompts that require a holistic spatio-temporal understanding: MLLMs struggle to answer prompts that refer to 1) the entirety of an environment that an agent equipped with an MLLM can operate in; and simultaneously also refer to 2) recent actions that just happened and are encoded in a video clip. However, such a holistic spatio-temporal understanding is important for agents operating in the real world. Our solution involves development of a dedicated data collection pipeline and fine-tuning of an MLLM equipped with projectors to improve both spatial understanding of an environment and temporal understanding of recent observations. About the Speaker Alex Schwing is an Associate Professor at the University of Illinois at Urbana-Champaign working with talented students on artificial intelligence, generative AI, and computer vision topics. He received his B.S. and diploma in Electrical Engineering and Information Technology from the Technical University of Munich in 2006 and 2008 respectively, and obtained a PhD in Computer Science from ETH Zurich in 2014. Afterwards he joined University of Toronto as a postdoctoral fellow until 2016. His research interests are in the area of artificial intelligence, generative AI, and computer vision, where he has co-authored numerous papers on topics in scene understanding, inference and learning algorithms, deep learning, image and language processing, and generative modeling. His PhD thesis was awarded an ETH medal and his team’s research was awarded an NSF CAREER award. Beyond the Lab: Real-World Anomaly Detection for Agricultural Computer Vision Anomaly detection is transforming manufacturing and surveillance, but what about agriculture? Can AI actually detect plant diseases and pest damage early enough to make a difference? This talk demonstrates how anomaly detection identifies and localizes crop problems using coffee leaf health as our primary example. We'll start with the foundational theory, then examine how these models detect rust and miner damage in leaf imagery. The session includes a comprehensive hands-on workflow using the open-source FiftyOne computer vision toolkit, covering dataset curation, patch extraction, model training, and result visualization. You'll gain both theoretical understanding of anomaly detection in computer vision and practical experience applying these techniques to agricultural challenges and other domains. About the Speaker Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.	Oct 15 - Visual AI in Agriculture (Day 1)
Oct 15 - Visual AI in Agriculture (Day 1) 2025-10-15 · 16:00 Join us for day one of a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI in Agriculture. Date and Time Oct 15 at 9 AM Pacific Location Virtual. Register for the Zoom. Paved2Paradise: Scalable LiDAR Simulation for Real-World Perception Training robust perception models for robotics and autonomy often requires massive, diverse 3D datasets. But collecting and annotating real-world LiDAR point clouds at scale is both expensive and time-consuming, especially when high-quality labels are needed. Paved2Paradise introduces a cost-effective alternative: a scalable LiDAR simulation pipeline that generates realistic, fully annotated datasets with minimal human labeling effort. The key idea is to “factor the real world” by separately capturing background scans (e.g., fields, roads, construction sites) and object scans (e.g., vehicles, people, machinery). By intelligently combining these two sources, Paved2Paradise can synthesize a combinatorially large set of diverse training scenes. The pipeline involves four steps: (1) collecting extensive background LiDAR scans, (2) recording high-resolution scans of target objects under controlled conditions, (3) inserting objects into backgrounds with physically consistent placement and occlusion, and (4) simulating LiDAR geometry to ensure realism. Experiments show that models trained on Paved2Paradise-generated data transfer effectively to the real world, achieving strong detection performance with far less manual annotation compared to conventional dataset collection. The approach is not only cost-efficient, but also flexible—allowing practitioners to easily expand to new object classes or domains by swapping in new background or object scans. For ML practitioners working in robotics, autonomous vehicles, or safety-critical perception, Paved2Paradise highlights a practical path toward scaling training data without scaling costs. It bridges the gap between simulation and real-world performance, enabling faster iteration and more reliable deployment of perception models. About the Speaker Michael A. Alcorn is a Senior Machine Learning Engineer at John Deere\, where he develops deep learning models for LiDAR and RGB perception in safety-critical\, real-time systems. He earned his Ph.D. in Computer Science from Auburn University\, with a dissertation on improving computer vision and spatiotemporal deep neural networks\, and also holds a Graduate Minor in Mathematics. Michael’s research has been cited by researchers at DeepMind\, Google\, Meta\, Microsoft\, and OpenAI\, among others\, and his (batter\\|pitcher)2vec paper was a prize-winner at the 2018 MIT Sloan Sports Analytics Conference. He has also contributed machine learning code to scikit-learn and Apache Solr\, and his GitHub repositories—which have collectively received over 2\,100 stars—have served as starting points for research and production code at many different organizations. MothBox: inexpensive, open-source, automated insect monitor Dr. Andy Quitmeyer will talk about the design of an exciting new open source science tool, The Mothbox. The Mothbox is an award winning project for broad scale monitoring of insects for biodiversity. It's a low cost device developed in harsh Panamanian jungles which takes super high resolution photos to then automatically ID the levels of biodiversity in forests and agriculture. After thousands of insect observations and hundreds of deployments in Panama, Peru, Mexico, Ecuador, and the US, we are now developing a new, manufacturable version to share this important tool worldwide. We will discuss the development of this device in the jungles of Panama and its importance to studying biodiversity worldwide. About the Speaker Dr. Andy Quitmeyer designs new ways to interact with the natural world. He has worked with large organizations like Cartoon Network, IDEO, and the Smithsonian, taught as a tenure-track professor at the National University of Singapore, and even had his research turned into a (silly) television series called “Hacking the Wild,” distributed by Discovery Networks. Now, he spends most of his time volunteering with smaller organizations, and recently founded the field-station makerspace, Digital Naturalism Laboratories. In the rainforest of Gamboa, Panama, Dinalab blends biological fieldwork and technological crafting with a community of local and international scientists, artists, engineers, and animal rehabilitators. He currently also advises students as an affiliate professor at the University of Washington. Foundation Models for Visual AI in Agriculture Foundation models have enabled a new way to address tasks, by benefitting from emerging capabilities in a zero-shot manner. In this talk I will discuss recent research on enabling visual AI in a zero-shot manner and via fine-tuning. Specifically, I will discuss joint work on RELOCATE, a simple training-free baseline designed to perform the challenging task of visual query localization in long videos. To eliminate the need for task-specific training and efficiently handle long videos, RELOCATE leverages a region-based representation derived from pretrained vision models. I will also discuss joint work on enabling multi-modal large language models (MLLMs) to correctly answer prompts that require a holistic spatio-temporal understanding: MLLMs struggle to answer prompts that refer to 1) the entirety of an environment that an agent equipped with an MLLM can operate in; and simultaneously also refer to 2) recent actions that just happened and are encoded in a video clip. However, such a holistic spatio-temporal understanding is important for agents operating in the real world. Our solution involves development of a dedicated data collection pipeline and fine-tuning of an MLLM equipped with projectors to improve both spatial understanding of an environment and temporal understanding of recent observations. About the Speaker Alex Schwing is an Associate Professor at the University of Illinois at Urbana-Champaign working with talented students on artificial intelligence, generative AI, and computer vision topics. He received his B.S. and diploma in Electrical Engineering and Information Technology from the Technical University of Munich in 2006 and 2008 respectively, and obtained a PhD in Computer Science from ETH Zurich in 2014. Afterwards he joined University of Toronto as a postdoctoral fellow until 2016. His research interests are in the area of artificial intelligence, generative AI, and computer vision, where he has co-authored numerous papers on topics in scene understanding, inference and learning algorithms, deep learning, image and language processing, and generative modeling. His PhD thesis was awarded an ETH medal and his team’s research was awarded an NSF CAREER award. Beyond the Lab: Real-World Anomaly Detection for Agricultural Computer Vision Anomaly detection is transforming manufacturing and surveillance, but what about agriculture? Can AI actually detect plant diseases and pest damage early enough to make a difference? This talk demonstrates how anomaly detection identifies and localizes crop problems using coffee leaf health as our primary example. We'll start with the foundational theory, then examine how these models detect rust and miner damage in leaf imagery. The session includes a comprehensive hands-on workflow using the open-source FiftyOne computer vision toolkit, covering dataset curation, patch extraction, model training, and result visualization. You'll gain both theoretical understanding of anomaly detection in computer vision and practical experience applying these techniques to agricultural challenges and other domains. About the Speaker Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.	Oct 15 - Visual AI in Agriculture (Day 1)
Oct 15 - Visual AI in Agriculture (Day 1) 2025-10-15 · 16:00 Join us for day one of a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI in Agriculture. Date and Time Oct 15 at 9 AM Pacific Location Virtual. Register for the Zoom. Paved2Paradise: Scalable LiDAR Simulation for Real-World Perception Training robust perception models for robotics and autonomy often requires massive, diverse 3D datasets. But collecting and annotating real-world LiDAR point clouds at scale is both expensive and time-consuming, especially when high-quality labels are needed. Paved2Paradise introduces a cost-effective alternative: a scalable LiDAR simulation pipeline that generates realistic, fully annotated datasets with minimal human labeling effort. The key idea is to “factor the real world” by separately capturing background scans (e.g., fields, roads, construction sites) and object scans (e.g., vehicles, people, machinery). By intelligently combining these two sources, Paved2Paradise can synthesize a combinatorially large set of diverse training scenes. The pipeline involves four steps: (1) collecting extensive background LiDAR scans, (2) recording high-resolution scans of target objects under controlled conditions, (3) inserting objects into backgrounds with physically consistent placement and occlusion, and (4) simulating LiDAR geometry to ensure realism. Experiments show that models trained on Paved2Paradise-generated data transfer effectively to the real world, achieving strong detection performance with far less manual annotation compared to conventional dataset collection. The approach is not only cost-efficient, but also flexible—allowing practitioners to easily expand to new object classes or domains by swapping in new background or object scans. For ML practitioners working in robotics, autonomous vehicles, or safety-critical perception, Paved2Paradise highlights a practical path toward scaling training data without scaling costs. It bridges the gap between simulation and real-world performance, enabling faster iteration and more reliable deployment of perception models. About the Speaker Michael A. Alcorn is a Senior Machine Learning Engineer at John Deere\, where he develops deep learning models for LiDAR and RGB perception in safety-critical\, real-time systems. He earned his Ph.D. in Computer Science from Auburn University\, with a dissertation on improving computer vision and spatiotemporal deep neural networks\, and also holds a Graduate Minor in Mathematics. Michael’s research has been cited by researchers at DeepMind\, Google\, Meta\, Microsoft\, and OpenAI\, among others\, and his (batter\\|pitcher)2vec paper was a prize-winner at the 2018 MIT Sloan Sports Analytics Conference. He has also contributed machine learning code to scikit-learn and Apache Solr\, and his GitHub repositories—which have collectively received over 2\,100 stars—have served as starting points for research and production code at many different organizations. MothBox: inexpensive, open-source, automated insect monitor Dr. Andy Quitmeyer will talk about the design of an exciting new open source science tool, The Mothbox. The Mothbox is an award winning project for broad scale monitoring of insects for biodiversity. It's a low cost device developed in harsh Panamanian jungles which takes super high resolution photos to then automatically ID the levels of biodiversity in forests and agriculture. After thousands of insect observations and hundreds of deployments in Panama, Peru, Mexico, Ecuador, and the US, we are now developing a new, manufacturable version to share this important tool worldwide. We will discuss the development of this device in the jungles of Panama and its importance to studying biodiversity worldwide. About the Speaker Dr. Andy Quitmeyer designs new ways to interact with the natural world. He has worked with large organizations like Cartoon Network, IDEO, and the Smithsonian, taught as a tenure-track professor at the National University of Singapore, and even had his research turned into a (silly) television series called “Hacking the Wild,” distributed by Discovery Networks. Now, he spends most of his time volunteering with smaller organizations, and recently founded the field-station makerspace, Digital Naturalism Laboratories. In the rainforest of Gamboa, Panama, Dinalab blends biological fieldwork and technological crafting with a community of local and international scientists, artists, engineers, and animal rehabilitators. He currently also advises students as an affiliate professor at the University of Washington. Foundation Models for Visual AI in Agriculture Foundation models have enabled a new way to address tasks, by benefitting from emerging capabilities in a zero-shot manner. In this talk I will discuss recent research on enabling visual AI in a zero-shot manner and via fine-tuning. Specifically, I will discuss joint work on RELOCATE, a simple training-free baseline designed to perform the challenging task of visual query localization in long videos. To eliminate the need for task-specific training and efficiently handle long videos, RELOCATE leverages a region-based representation derived from pretrained vision models. I will also discuss joint work on enabling multi-modal large language models (MLLMs) to correctly answer prompts that require a holistic spatio-temporal understanding: MLLMs struggle to answer prompts that refer to 1) the entirety of an environment that an agent equipped with an MLLM can operate in; and simultaneously also refer to 2) recent actions that just happened and are encoded in a video clip. However, such a holistic spatio-temporal understanding is important for agents operating in the real world. Our solution involves development of a dedicated data collection pipeline and fine-tuning of an MLLM equipped with projectors to improve both spatial understanding of an environment and temporal understanding of recent observations. About the Speaker Alex Schwing is an Associate Professor at the University of Illinois at Urbana-Champaign working with talented students on artificial intelligence, generative AI, and computer vision topics. He received his B.S. and diploma in Electrical Engineering and Information Technology from the Technical University of Munich in 2006 and 2008 respectively, and obtained a PhD in Computer Science from ETH Zurich in 2014. Afterwards he joined University of Toronto as a postdoctoral fellow until 2016. His research interests are in the area of artificial intelligence, generative AI, and computer vision, where he has co-authored numerous papers on topics in scene understanding, inference and learning algorithms, deep learning, image and language processing, and generative modeling. His PhD thesis was awarded an ETH medal and his team’s research was awarded an NSF CAREER award. Beyond the Lab: Real-World Anomaly Detection for Agricultural Computer Vision Anomaly detection is transforming manufacturing and surveillance, but what about agriculture? Can AI actually detect plant diseases and pest damage early enough to make a difference? This talk demonstrates how anomaly detection identifies and localizes crop problems using coffee leaf health as our primary example. We'll start with the foundational theory, then examine how these models detect rust and miner damage in leaf imagery. The session includes a comprehensive hands-on workflow using the open-source FiftyOne computer vision toolkit, covering dataset curation, patch extraction, model training, and result visualization. You'll gain both theoretical understanding of anomaly detection in computer vision and practical experience applying these techniques to agricultural challenges and other domains. About the Speaker Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.	Oct 15 - Visual AI in Agriculture (Day 1)
Oct 15 - Visual AI in Agriculture (Day 1) 2025-10-15 · 16:00 Join us for day one of a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI in Agriculture. Date and Time Oct 15 at 9 AM Pacific Location Virtual. Register for the Zoom. Paved2Paradise: Scalable LiDAR Simulation for Real-World Perception Training robust perception models for robotics and autonomy often requires massive, diverse 3D datasets. But collecting and annotating real-world LiDAR point clouds at scale is both expensive and time-consuming, especially when high-quality labels are needed. Paved2Paradise introduces a cost-effective alternative: a scalable LiDAR simulation pipeline that generates realistic, fully annotated datasets with minimal human labeling effort. The key idea is to “factor the real world” by separately capturing background scans (e.g., fields, roads, construction sites) and object scans (e.g., vehicles, people, machinery). By intelligently combining these two sources, Paved2Paradise can synthesize a combinatorially large set of diverse training scenes. The pipeline involves four steps: (1) collecting extensive background LiDAR scans, (2) recording high-resolution scans of target objects under controlled conditions, (3) inserting objects into backgrounds with physically consistent placement and occlusion, and (4) simulating LiDAR geometry to ensure realism. Experiments show that models trained on Paved2Paradise-generated data transfer effectively to the real world, achieving strong detection performance with far less manual annotation compared to conventional dataset collection. The approach is not only cost-efficient, but also flexible—allowing practitioners to easily expand to new object classes or domains by swapping in new background or object scans. For ML practitioners working in robotics, autonomous vehicles, or safety-critical perception, Paved2Paradise highlights a practical path toward scaling training data without scaling costs. It bridges the gap between simulation and real-world performance, enabling faster iteration and more reliable deployment of perception models. About the Speaker Michael A. Alcorn is a Senior Machine Learning Engineer at John Deere\, where he develops deep learning models for LiDAR and RGB perception in safety-critical\, real-time systems. He earned his Ph.D. in Computer Science from Auburn University\, with a dissertation on improving computer vision and spatiotemporal deep neural networks\, and also holds a Graduate Minor in Mathematics. Michael’s research has been cited by researchers at DeepMind\, Google\, Meta\, Microsoft\, and OpenAI\, among others\, and his (batter\\|pitcher)2vec paper was a prize-winner at the 2018 MIT Sloan Sports Analytics Conference. He has also contributed machine learning code to scikit-learn and Apache Solr\, and his GitHub repositories—which have collectively received over 2\,100 stars—have served as starting points for research and production code at many different organizations. MothBox: inexpensive, open-source, automated insect monitor Dr. Andy Quitmeyer will talk about the design of an exciting new open source science tool, The Mothbox. The Mothbox is an award winning project for broad scale monitoring of insects for biodiversity. It's a low cost device developed in harsh Panamanian jungles which takes super high resolution photos to then automatically ID the levels of biodiversity in forests and agriculture. After thousands of insect observations and hundreds of deployments in Panama, Peru, Mexico, Ecuador, and the US, we are now developing a new, manufacturable version to share this important tool worldwide. We will discuss the development of this device in the jungles of Panama and its importance to studying biodiversity worldwide. About the Speaker Dr. Andy Quitmeyer designs new ways to interact with the natural world. He has worked with large organizations like Cartoon Network, IDEO, and the Smithsonian, taught as a tenure-track professor at the National University of Singapore, and even had his research turned into a (silly) television series called “Hacking the Wild,” distributed by Discovery Networks. Now, he spends most of his time volunteering with smaller organizations, and recently founded the field-station makerspace, Digital Naturalism Laboratories. In the rainforest of Gamboa, Panama, Dinalab blends biological fieldwork and technological crafting with a community of local and international scientists, artists, engineers, and animal rehabilitators. He currently also advises students as an affiliate professor at the University of Washington. Foundation Models for Visual AI in Agriculture Foundation models have enabled a new way to address tasks, by benefitting from emerging capabilities in a zero-shot manner. In this talk I will discuss recent research on enabling visual AI in a zero-shot manner and via fine-tuning. Specifically, I will discuss joint work on RELOCATE, a simple training-free baseline designed to perform the challenging task of visual query localization in long videos. To eliminate the need for task-specific training and efficiently handle long videos, RELOCATE leverages a region-based representation derived from pretrained vision models. I will also discuss joint work on enabling multi-modal large language models (MLLMs) to correctly answer prompts that require a holistic spatio-temporal understanding: MLLMs struggle to answer prompts that refer to 1) the entirety of an environment that an agent equipped with an MLLM can operate in; and simultaneously also refer to 2) recent actions that just happened and are encoded in a video clip. However, such a holistic spatio-temporal understanding is important for agents operating in the real world. Our solution involves development of a dedicated data collection pipeline and fine-tuning of an MLLM equipped with projectors to improve both spatial understanding of an environment and temporal understanding of recent observations. About the Speaker Alex Schwing is an Associate Professor at the University of Illinois at Urbana-Champaign working with talented students on artificial intelligence, generative AI, and computer vision topics. He received his B.S. and diploma in Electrical Engineering and Information Technology from the Technical University of Munich in 2006 and 2008 respectively, and obtained a PhD in Computer Science from ETH Zurich in 2014. Afterwards he joined University of Toronto as a postdoctoral fellow until 2016. His research interests are in the area of artificial intelligence, generative AI, and computer vision, where he has co-authored numerous papers on topics in scene understanding, inference and learning algorithms, deep learning, image and language processing, and generative modeling. His PhD thesis was awarded an ETH medal and his team’s research was awarded an NSF CAREER award. Beyond the Lab: Real-World Anomaly Detection for Agricultural Computer Vision Anomaly detection is transforming manufacturing and surveillance, but what about agriculture? Can AI actually detect plant diseases and pest damage early enough to make a difference? This talk demonstrates how anomaly detection identifies and localizes crop problems using coffee leaf health as our primary example. We'll start with the foundational theory, then examine how these models detect rust and miner damage in leaf imagery. The session includes a comprehensive hands-on workflow using the open-source FiftyOne computer vision toolkit, covering dataset curation, patch extraction, model training, and result visualization. You'll gain both theoretical understanding of anomaly detection in computer vision and practical experience applying these techniques to agricultural challenges and other domains. About the Speaker Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.	Oct 15 - Visual AI in Agriculture (Day 1)
Oct 2 - Women in AI Virtual Event 2025-10-02 · 16:00 Hear talks from experts on the latest topics in AI, ML, and computer vision. Date and Time Oct 2 at 9 AM Pacific Location Virtual. Register for the Zoom. The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems. About the Speaker Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones. Managing Medical Imaging Datasets: From Curation to Evaluation High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment. We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities. Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact. About the Speaker Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia. Building Agents That Learn: Managing Memory in AI Agents In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time. About the Speaker Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops. Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments. About the Speaker Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.	Oct 2 - Women in AI Virtual Event
Oct 2 - Women in AI Virtual Event 2025-10-02 · 16:00 Hear talks from experts on the latest topics in AI, ML, and computer vision. Date and Time Oct 2 at 9 AM Pacific Location Virtual. Register for the Zoom. The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems. About the Speaker Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones. Managing Medical Imaging Datasets: From Curation to Evaluation High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment. We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities. Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact. About the Speaker Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia. Building Agents That Learn: Managing Memory in AI Agents In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time. About the Speaker Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops. Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments. About the Speaker Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.	Oct 2 - Women in AI Virtual Event
Oct 2 - Women in AI Virtual Event 2025-10-02 · 16:00 Hear talks from experts on the latest topics in AI, ML, and computer vision. Date and Time Oct 2 at 9 AM Pacific Location Virtual. Register for the Zoom. The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems. About the Speaker Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones. Managing Medical Imaging Datasets: From Curation to Evaluation High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment. We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities. Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact. About the Speaker Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia. Building Agents That Learn: Managing Memory in AI Agents In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time. About the Speaker Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops. Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments. About the Speaker Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.	Oct 2 - Women in AI Virtual Event
Oct 2 - Women in AI Virtual Event 2025-10-02 · 16:00 Hear talks from experts on the latest topics in AI, ML, and computer vision. Date and Time Oct 2 at 9 AM Pacific Location Virtual. Register for the Zoom. The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems. About the Speaker Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones. Managing Medical Imaging Datasets: From Curation to Evaluation High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment. We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities. Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact. About the Speaker Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia. Building Agents That Learn: Managing Memory in AI Agents In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time. About the Speaker Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops. Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments. About the Speaker Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.	Oct 2 - Women in AI Virtual Event
Oct 2 - Women in AI Virtual Event 2025-10-02 · 16:00 Hear talks from experts on the latest topics in AI, ML, and computer vision. Date and Time Oct 2 at 9 AM Pacific Location Virtual. Register for the Zoom. The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems. About the Speaker Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones. Managing Medical Imaging Datasets: From Curation to Evaluation High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment. We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities. Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact. About the Speaker Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia. Building Agents That Learn: Managing Memory in AI Agents In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time. About the Speaker Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops. Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments. About the Speaker Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.	Oct 2 - Women in AI Virtual Event
Oct 2 - Women in AI Virtual Event 2025-10-02 · 16:00 Hear talks from experts on the latest topics in AI, ML, and computer vision. Date and Time Oct 2 at 9 AM Pacific Location Virtual. Register for the Zoom. The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems. About the Speaker Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones. Managing Medical Imaging Datasets: From Curation to Evaluation High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment. We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities. Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact. About the Speaker Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia. Building Agents That Learn: Managing Memory in AI Agents In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time. About the Speaker Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops. Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments. About the Speaker Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.	Oct 2 - Women in AI Virtual Event

Nov 13 - Women in AI 2025-11-13 · 17:00

Hear talks from experts on the latest topics in AI, ML, and computer vision on November 13.

Date and Location

Nov 13, 2025 9 AM Pacific Online. Register for the Zoom!

Copy, Paste, Customize! The Template Approach to AI Engineering

Most AI implementations fail because teams treat prompt engineering as ad-hoc experimentation rather than systematic software engineering, leading to unreliable systems that don't scale beyond proof-of-concepts. This talk demonstrates engineering practices that enable reliable AI deployment through standardized prompt templates, systematic validation frameworks, and production observability.

Drawing from experience developing fillable prompt templates currently being validated in production environments processing thousands of submissions, I'll share how Infrastructure as Code principles apply to LLM workflows, why evaluation metrics like BLEU scores are critical for production reliability, and how systematic failure analysis prevents costly deployment issues. Attendees will walk away with understanding of practical frameworks for improving AI system reliability and specific strategies for building more consistent, scalable AI implementations.

About the Speaker

Jeanne McClure is a postdoctoral scholar at NC State's Data Science and AI Academy with expertise in systematic AI implementation and validation. Her research transforms experimental AI tools into reliable production systems through standardized prompt templates, rigorous testing frameworks, and systematic failure analysis. She holds a PhD in Learning, Design and Technology with additional graduate work in data science.

Multimodality with Biases: Understand and Evaluate VLMs for Autonomous Driving with FiftyOne

Do your VLMs really see danger? With FiftyOne, I’ll show you how to understand and evaluate vision-language models for autonomous driving — making risk and bias visible in seconds. We’ll compare models on the same scenes, reveal failures and edge cases, and you’ll see a simple dashboard to decide which data to curate and what to adjust. You’ll leave with a clear, practical, and replicable method to raise the bar for safety.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

The Heart of Innovation: Women, AI, and the Future of Healthcare

This session explores how Artificial Intelligence is transforming healthcare by enhancing diagnosis, treatment, and patient outcomes. It highlights the importance of diverse and female perspectives in shaping AI solutions that are ethical, empathetic, and human-centered. We will discuss key applications, current challenges, and the future potential of AI in medicine. It’s a forward-looking conversation about how innovation can build a healthier world.

About the Speaker

Karen Sanchez is a Postdoctoral Researcher at the Center of Excellence for Generative AI at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Her research focuses on AI for Science, spanning computer vision, video understanding, and privacy-preserving machine learning. She is also an active advocate for diversity and outreach in AI, contributing to global initiatives that connect researchers and amplify underrepresented voices in technology.

Language Diffusion Models

Autoregressive models (ARMs) are widely regarded as the cornerstone of large language models (LLMs). Challenge this notion by introducing LLaDA, a diffusion model trained from scratch under the pre-training and supervised fine-tuning (SFT) paradigm. LLaDA models distributions through a forward data masking process and a reverse process, parameterized by a vanilla Transformer to predict masked tokens.

Optimizing a likelihood bound provides a principled generative approach for probabilistic inference. Across extensive benchmarks, LLaDA demonstrates strong scalability, outperforming self-constructed ARM baselines. Remarkably, LLaDA 8B is competitive with strong LLMs like LLaMA3 8B in in-context learning and, after SFT, exhibits impressive instruction-following abilities in case studies such as multi-turn dialogue.

About the Speaker

Jayita Bhattacharyya is an AI/ML Nerd with a blend of technical speaking & hackathon wizardry! Applying tech to solve real-world problems. The work focus these days is on generative AI. Helping software teams incorporate AI into transforming software engineering.

Nov 13 - Women in AI

Nov 13 - Women in AI 2025-11-13 · 17:00

Hear talks from experts on the latest topics in AI, ML, and computer vision on November 13.

Date and Location

Nov 13, 2025 9 AM Pacific Online. Register for the Zoom!

Copy, Paste, Customize! The Template Approach to AI Engineering

Most AI implementations fail because teams treat prompt engineering as ad-hoc experimentation rather than systematic software engineering, leading to unreliable systems that don't scale beyond proof-of-concepts. This talk demonstrates engineering practices that enable reliable AI deployment through standardized prompt templates, systematic validation frameworks, and production observability.

Drawing from experience developing fillable prompt templates currently being validated in production environments processing thousands of submissions, I'll share how Infrastructure as Code principles apply to LLM workflows, why evaluation metrics like BLEU scores are critical for production reliability, and how systematic failure analysis prevents costly deployment issues. Attendees will walk away with understanding of practical frameworks for improving AI system reliability and specific strategies for building more consistent, scalable AI implementations.

About the Speaker

Jeanne McClure is a postdoctoral scholar at NC State's Data Science and AI Academy with expertise in systematic AI implementation and validation. Her research transforms experimental AI tools into reliable production systems through standardized prompt templates, rigorous testing frameworks, and systematic failure analysis. She holds a PhD in Learning, Design and Technology with additional graduate work in data science.

Multimodality with Biases: Understand and Evaluate VLMs for Autonomous Driving with FiftyOne

Do your VLMs really see danger? With FiftyOne, I’ll show you how to understand and evaluate vision-language models for autonomous driving — making risk and bias visible in seconds. We’ll compare models on the same scenes, reveal failures and edge cases, and you’ll see a simple dashboard to decide which data to curate and what to adjust. You’ll leave with a clear, practical, and replicable method to raise the bar for safety.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

The Heart of Innovation: Women, AI, and the Future of Healthcare

This session explores how Artificial Intelligence is transforming healthcare by enhancing diagnosis, treatment, and patient outcomes. It highlights the importance of diverse and female perspectives in shaping AI solutions that are ethical, empathetic, and human-centered. We will discuss key applications, current challenges, and the future potential of AI in medicine. It’s a forward-looking conversation about how innovation can build a healthier world.

About the Speaker

Karen Sanchez is a Postdoctoral Researcher at the Center of Excellence for Generative AI at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Her research focuses on AI for Science, spanning computer vision, video understanding, and privacy-preserving machine learning. She is also an active advocate for diversity and outreach in AI, contributing to global initiatives that connect researchers and amplify underrepresented voices in technology.

Language Diffusion Models

Autoregressive models (ARMs) are widely regarded as the cornerstone of large language models (LLMs). Challenge this notion by introducing LLaDA, a diffusion model trained from scratch under the pre-training and supervised fine-tuning (SFT) paradigm. LLaDA models distributions through a forward data masking process and a reverse process, parameterized by a vanilla Transformer to predict masked tokens.

Optimizing a likelihood bound provides a principled generative approach for probabilistic inference. Across extensive benchmarks, LLaDA demonstrates strong scalability, outperforming self-constructed ARM baselines. Remarkably, LLaDA 8B is competitive with strong LLMs like LLaMA3 8B in in-context learning and, after SFT, exhibits impressive instruction-following abilities in case studies such as multi-turn dialogue.

About the Speaker

Jayita Bhattacharyya is an AI/ML Nerd with a blend of technical speaking & hackathon wizardry! Applying tech to solve real-world problems. The work focus these days is on generative AI. Helping software teams incorporate AI into transforming software engineering.

Nov 13 - Women in AI

Nov 13 - Women in AI 2025-11-13 · 17:00

Hear talks from experts on the latest topics in AI, ML, and computer vision on November 13.

Date and Location

Nov 13, 2025 9 AM Pacific Online. Register for the Zoom!

Copy, Paste, Customize! The Template Approach to AI Engineering

Most AI implementations fail because teams treat prompt engineering as ad-hoc experimentation rather than systematic software engineering, leading to unreliable systems that don't scale beyond proof-of-concepts. This talk demonstrates engineering practices that enable reliable AI deployment through standardized prompt templates, systematic validation frameworks, and production observability.

Drawing from experience developing fillable prompt templates currently being validated in production environments processing thousands of submissions, I'll share how Infrastructure as Code principles apply to LLM workflows, why evaluation metrics like BLEU scores are critical for production reliability, and how systematic failure analysis prevents costly deployment issues. Attendees will walk away with understanding of practical frameworks for improving AI system reliability and specific strategies for building more consistent, scalable AI implementations.

About the Speaker

Jeanne McClure is a postdoctoral scholar at NC State's Data Science and AI Academy with expertise in systematic AI implementation and validation. Her research transforms experimental AI tools into reliable production systems through standardized prompt templates, rigorous testing frameworks, and systematic failure analysis. She holds a PhD in Learning, Design and Technology with additional graduate work in data science.

Multimodality with Biases: Understand and Evaluate VLMs for Autonomous Driving with FiftyOne

Do your VLMs really see danger? With FiftyOne, I’ll show you how to understand and evaluate vision-language models for autonomous driving — making risk and bias visible in seconds. We’ll compare models on the same scenes, reveal failures and edge cases, and you’ll see a simple dashboard to decide which data to curate and what to adjust. You’ll leave with a clear, practical, and replicable method to raise the bar for safety.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

The Heart of Innovation: Women, AI, and the Future of Healthcare

This session explores how Artificial Intelligence is transforming healthcare by enhancing diagnosis, treatment, and patient outcomes. It highlights the importance of diverse and female perspectives in shaping AI solutions that are ethical, empathetic, and human-centered. We will discuss key applications, current challenges, and the future potential of AI in medicine. It’s a forward-looking conversation about how innovation can build a healthier world.

About the Speaker

Karen Sanchez is a Postdoctoral Researcher at the Center of Excellence for Generative AI at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Her research focuses on AI for Science, spanning computer vision, video understanding, and privacy-preserving machine learning. She is also an active advocate for diversity and outreach in AI, contributing to global initiatives that connect researchers and amplify underrepresented voices in technology.

Language Diffusion Models

Autoregressive models (ARMs) are widely regarded as the cornerstone of large language models (LLMs). Challenge this notion by introducing LLaDA, a diffusion model trained from scratch under the pre-training and supervised fine-tuning (SFT) paradigm. LLaDA models distributions through a forward data masking process and a reverse process, parameterized by a vanilla Transformer to predict masked tokens.

Optimizing a likelihood bound provides a principled generative approach for probabilistic inference. Across extensive benchmarks, LLaDA demonstrates strong scalability, outperforming self-constructed ARM baselines. Remarkably, LLaDA 8B is competitive with strong LLMs like LLaMA3 8B in in-context learning and, after SFT, exhibits impressive instruction-following abilities in case studies such as multi-turn dialogue.

About the Speaker

Jayita Bhattacharyya is an AI/ML Nerd with a blend of technical speaking & hackathon wizardry! Applying tech to solve real-world problems. The work focus these days is on generative AI. Helping software teams incorporate AI into transforming software engineering.

Nov 13 - Women in AI

Nov 13 - Women in AI 2025-11-13 · 17:00

Hear talks from experts on the latest topics in AI, ML, and computer vision on November 13.

Date and Location

Nov 13, 2025 9 AM Pacific Online. Register for the Zoom!

Copy, Paste, Customize! The Template Approach to AI Engineering

Most AI implementations fail because teams treat prompt engineering as ad-hoc experimentation rather than systematic software engineering, leading to unreliable systems that don't scale beyond proof-of-concepts. This talk demonstrates engineering practices that enable reliable AI deployment through standardized prompt templates, systematic validation frameworks, and production observability.

Drawing from experience developing fillable prompt templates currently being validated in production environments processing thousands of submissions, I'll share how Infrastructure as Code principles apply to LLM workflows, why evaluation metrics like BLEU scores are critical for production reliability, and how systematic failure analysis prevents costly deployment issues. Attendees will walk away with understanding of practical frameworks for improving AI system reliability and specific strategies for building more consistent, scalable AI implementations.

About the Speaker

Jeanne McClure is a postdoctoral scholar at NC State's Data Science and AI Academy with expertise in systematic AI implementation and validation. Her research transforms experimental AI tools into reliable production systems through standardized prompt templates, rigorous testing frameworks, and systematic failure analysis. She holds a PhD in Learning, Design and Technology with additional graduate work in data science.

Multimodality with Biases: Understand and Evaluate VLMs for Autonomous Driving with FiftyOne

Do your VLMs really see danger? With FiftyOne, I’ll show you how to understand and evaluate vision-language models for autonomous driving — making risk and bias visible in seconds. We’ll compare models on the same scenes, reveal failures and edge cases, and you’ll see a simple dashboard to decide which data to curate and what to adjust. You’ll leave with a clear, practical, and replicable method to raise the bar for safety.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

The Heart of Innovation: Women, AI, and the Future of Healthcare

This session explores how Artificial Intelligence is transforming healthcare by enhancing diagnosis, treatment, and patient outcomes. It highlights the importance of diverse and female perspectives in shaping AI solutions that are ethical, empathetic, and human-centered. We will discuss key applications, current challenges, and the future potential of AI in medicine. It’s a forward-looking conversation about how innovation can build a healthier world.

About the Speaker

Karen Sanchez is a Postdoctoral Researcher at the Center of Excellence for Generative AI at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Her research focuses on AI for Science, spanning computer vision, video understanding, and privacy-preserving machine learning. She is also an active advocate for diversity and outreach in AI, contributing to global initiatives that connect researchers and amplify underrepresented voices in technology.

Language Diffusion Models

Autoregressive models (ARMs) are widely regarded as the cornerstone of large language models (LLMs). Challenge this notion by introducing LLaDA, a diffusion model trained from scratch under the pre-training and supervised fine-tuning (SFT) paradigm. LLaDA models distributions through a forward data masking process and a reverse process, parameterized by a vanilla Transformer to predict masked tokens.

Optimizing a likelihood bound provides a principled generative approach for probabilistic inference. Across extensive benchmarks, LLaDA demonstrates strong scalability, outperforming self-constructed ARM baselines. Remarkably, LLaDA 8B is competitive with strong LLMs like LLaMA3 8B in in-context learning and, after SFT, exhibits impressive instruction-following abilities in case studies such as multi-turn dialogue.

About the Speaker

Jayita Bhattacharyya is an AI/ML Nerd with a blend of technical speaking & hackathon wizardry! Applying tech to solve real-world problems. The work focus these days is on generative AI. Helping software teams incorporate AI into transforming software engineering.

Nov 13 - Women in AI

Nov 13 - Women in AI 2025-11-13 · 17:00

Hear talks from experts on the latest topics in AI, ML, and computer vision on November 13.

Date and Location

Nov 13, 2025 9 AM Pacific Online. Register for the Zoom!

Copy, Paste, Customize! The Template Approach to AI Engineering

Most AI implementations fail because teams treat prompt engineering as ad-hoc experimentation rather than systematic software engineering, leading to unreliable systems that don't scale beyond proof-of-concepts. This talk demonstrates engineering practices that enable reliable AI deployment through standardized prompt templates, systematic validation frameworks, and production observability.

Drawing from experience developing fillable prompt templates currently being validated in production environments processing thousands of submissions, I'll share how Infrastructure as Code principles apply to LLM workflows, why evaluation metrics like BLEU scores are critical for production reliability, and how systematic failure analysis prevents costly deployment issues. Attendees will walk away with understanding of practical frameworks for improving AI system reliability and specific strategies for building more consistent, scalable AI implementations.

About the Speaker

Jeanne McClure is a postdoctoral scholar at NC State's Data Science and AI Academy with expertise in systematic AI implementation and validation. Her research transforms experimental AI tools into reliable production systems through standardized prompt templates, rigorous testing frameworks, and systematic failure analysis. She holds a PhD in Learning, Design and Technology with additional graduate work in data science.

Multimodality with Biases: Understand and Evaluate VLMs for Autonomous Driving with FiftyOne

Do your VLMs really see danger? With FiftyOne, I’ll show you how to understand and evaluate vision-language models for autonomous driving — making risk and bias visible in seconds. We’ll compare models on the same scenes, reveal failures and edge cases, and you’ll see a simple dashboard to decide which data to curate and what to adjust. You’ll leave with a clear, practical, and replicable method to raise the bar for safety.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

The Heart of Innovation: Women, AI, and the Future of Healthcare

This session explores how Artificial Intelligence is transforming healthcare by enhancing diagnosis, treatment, and patient outcomes. It highlights the importance of diverse and female perspectives in shaping AI solutions that are ethical, empathetic, and human-centered. We will discuss key applications, current challenges, and the future potential of AI in medicine. It’s a forward-looking conversation about how innovation can build a healthier world.

About the Speaker

Karen Sanchez is a Postdoctoral Researcher at the Center of Excellence for Generative AI at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Her research focuses on AI for Science, spanning computer vision, video understanding, and privacy-preserving machine learning. She is also an active advocate for diversity and outreach in AI, contributing to global initiatives that connect researchers and amplify underrepresented voices in technology.

Language Diffusion Models

Autoregressive models (ARMs) are widely regarded as the cornerstone of large language models (LLMs). Challenge this notion by introducing LLaDA, a diffusion model trained from scratch under the pre-training and supervised fine-tuning (SFT) paradigm. LLaDA models distributions through a forward data masking process and a reverse process, parameterized by a vanilla Transformer to predict masked tokens.

Optimizing a likelihood bound provides a principled generative approach for probabilistic inference. Across extensive benchmarks, LLaDA demonstrates strong scalability, outperforming self-constructed ARM baselines. Remarkably, LLaDA 8B is competitive with strong LLMs like LLaMA3 8B in in-context learning and, after SFT, exhibits impressive instruction-following abilities in case studies such as multi-turn dialogue.

About the Speaker

Jayita Bhattacharyya is an AI/ML Nerd with a blend of technical speaking & hackathon wizardry! Applying tech to solve real-world problems. The work focus these days is on generative AI. Helping software teams incorporate AI into transforming software engineering.

Nov 13 - Women in AI

Nov 13 - Women in AI 2025-11-13 · 17:00

Hear talks from experts on the latest topics in AI, ML, and computer vision on November 13.

Date and Location

Nov 13, 2025 9 AM Pacific Online. Register for the Zoom!

Copy, Paste, Customize! The Template Approach to AI Engineering

Most AI implementations fail because teams treat prompt engineering as ad-hoc experimentation rather than systematic software engineering, leading to unreliable systems that don't scale beyond proof-of-concepts. This talk demonstrates engineering practices that enable reliable AI deployment through standardized prompt templates, systematic validation frameworks, and production observability.

Drawing from experience developing fillable prompt templates currently being validated in production environments processing thousands of submissions, I'll share how Infrastructure as Code principles apply to LLM workflows, why evaluation metrics like BLEU scores are critical for production reliability, and how systematic failure analysis prevents costly deployment issues. Attendees will walk away with understanding of practical frameworks for improving AI system reliability and specific strategies for building more consistent, scalable AI implementations.

About the Speaker

Jeanne McClure is a postdoctoral scholar at NC State's Data Science and AI Academy with expertise in systematic AI implementation and validation. Her research transforms experimental AI tools into reliable production systems through standardized prompt templates, rigorous testing frameworks, and systematic failure analysis. She holds a PhD in Learning, Design and Technology with additional graduate work in data science.

Multimodality with Biases: Understand and Evaluate VLMs for Autonomous Driving with FiftyOne

Do your VLMs really see danger? With FiftyOne, I’ll show you how to understand and evaluate vision-language models for autonomous driving — making risk and bias visible in seconds. We’ll compare models on the same scenes, reveal failures and edge cases, and you’ll see a simple dashboard to decide which data to curate and what to adjust. You’ll leave with a clear, practical, and replicable method to raise the bar for safety.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

The Heart of Innovation: Women, AI, and the Future of Healthcare

This session explores how Artificial Intelligence is transforming healthcare by enhancing diagnosis, treatment, and patient outcomes. It highlights the importance of diverse and female perspectives in shaping AI solutions that are ethical, empathetic, and human-centered. We will discuss key applications, current challenges, and the future potential of AI in medicine. It’s a forward-looking conversation about how innovation can build a healthier world.

About the Speaker

Karen Sanchez is a Postdoctoral Researcher at the Center of Excellence for Generative AI at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Her research focuses on AI for Science, spanning computer vision, video understanding, and privacy-preserving machine learning. She is also an active advocate for diversity and outreach in AI, contributing to global initiatives that connect researchers and amplify underrepresented voices in technology.

Language Diffusion Models

Autoregressive models (ARMs) are widely regarded as the cornerstone of large language models (LLMs). Challenge this notion by introducing LLaDA, a diffusion model trained from scratch under the pre-training and supervised fine-tuning (SFT) paradigm. LLaDA models distributions through a forward data masking process and a reverse process, parameterized by a vanilla Transformer to predict masked tokens.

Optimizing a likelihood bound provides a principled generative approach for probabilistic inference. Across extensive benchmarks, LLaDA demonstrates strong scalability, outperforming self-constructed ARM baselines. Remarkably, LLaDA 8B is competitive with strong LLMs like LLaMA3 8B in in-context learning and, after SFT, exhibits impressive instruction-following abilities in case studies such as multi-turn dialogue.

About the Speaker

Jayita Bhattacharyya is an AI/ML Nerd with a blend of technical speaking & hackathon wizardry! Applying tech to solve real-world problems. The work focus these days is on generative AI. Helping software teams incorporate AI into transforming software engineering.

Nov 13 - Women in AI

Nov 13 - Women in AI 2025-11-13 · 17:00

Hear talks from experts on the latest topics in AI, ML, and computer vision on November 13.

Date and Location

Nov 13, 2025 9 AM Pacific Online. Register for the Zoom!

Copy, Paste, Customize! The Template Approach to AI Engineering

Most AI implementations fail because teams treat prompt engineering as ad-hoc experimentation rather than systematic software engineering, leading to unreliable systems that don't scale beyond proof-of-concepts. This talk demonstrates engineering practices that enable reliable AI deployment through standardized prompt templates, systematic validation frameworks, and production observability.

Drawing from experience developing fillable prompt templates currently being validated in production environments processing thousands of submissions, I'll share how Infrastructure as Code principles apply to LLM workflows, why evaluation metrics like BLEU scores are critical for production reliability, and how systematic failure analysis prevents costly deployment issues. Attendees will walk away with understanding of practical frameworks for improving AI system reliability and specific strategies for building more consistent, scalable AI implementations.

About the Speaker

Jeanne McClure is a postdoctoral scholar at NC State's Data Science and AI Academy with expertise in systematic AI implementation and validation. Her research transforms experimental AI tools into reliable production systems through standardized prompt templates, rigorous testing frameworks, and systematic failure analysis. She holds a PhD in Learning, Design and Technology with additional graduate work in data science.

Multimodality with Biases: Understand and Evaluate VLMs for Autonomous Driving with FiftyOne

Do your VLMs really see danger? With FiftyOne, I’ll show you how to understand and evaluate vision-language models for autonomous driving — making risk and bias visible in seconds. We’ll compare models on the same scenes, reveal failures and edge cases, and you’ll see a simple dashboard to decide which data to curate and what to adjust. You’ll leave with a clear, practical, and replicable method to raise the bar for safety.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

The Heart of Innovation: Women, AI, and the Future of Healthcare

This session explores how Artificial Intelligence is transforming healthcare by enhancing diagnosis, treatment, and patient outcomes. It highlights the importance of diverse and female perspectives in shaping AI solutions that are ethical, empathetic, and human-centered. We will discuss key applications, current challenges, and the future potential of AI in medicine. It’s a forward-looking conversation about how innovation can build a healthier world.

About the Speaker

Karen Sanchez is a Postdoctoral Researcher at the Center of Excellence for Generative AI at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Her research focuses on AI for Science, spanning computer vision, video understanding, and privacy-preserving machine learning. She is also an active advocate for diversity and outreach in AI, contributing to global initiatives that connect researchers and amplify underrepresented voices in technology.

Language Diffusion Models

Autoregressive models (ARMs) are widely regarded as the cornerstone of large language models (LLMs). Challenge this notion by introducing LLaDA, a diffusion model trained from scratch under the pre-training and supervised fine-tuning (SFT) paradigm. LLaDA models distributions through a forward data masking process and a reverse process, parameterized by a vanilla Transformer to predict masked tokens.

Optimizing a likelihood bound provides a principled generative approach for probabilistic inference. Across extensive benchmarks, LLaDA demonstrates strong scalability, outperforming self-constructed ARM baselines. Remarkably, LLaDA 8B is competitive with strong LLMs like LLaMA3 8B in in-context learning and, after SFT, exhibits impressive instruction-following abilities in case studies such as multi-turn dialogue.

About the Speaker

Jayita Bhattacharyya is an AI/ML Nerd with a blend of technical speaking & hackathon wizardry! Applying tech to solve real-world problems. The work focus these days is on generative AI. Helping software teams incorporate AI into transforming software engineering.

Nov 13 - Women in AI

Oct 15 - Visual AI in Agriculture (Day 1) 2025-10-15 · 16:00

Join us for day one of a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI in Agriculture.

Date and Time Oct 15 at 9 AM Pacific

Location Virtual. Register for the Zoom.

Paved2Paradise: Scalable LiDAR Simulation for Real-World Perception

Training robust perception models for robotics and autonomy often requires massive, diverse 3D datasets. But collecting and annotating real-world LiDAR point clouds at scale is both expensive and time-consuming, especially when high-quality labels are needed. Paved2Paradise introduces a cost-effective alternative: a scalable LiDAR simulation pipeline that generates realistic, fully annotated datasets with minimal human labeling effort.

The key idea is to “factor the real world” by separately capturing background scans (e.g., fields, roads, construction sites) and object scans (e.g., vehicles, people, machinery). By intelligently combining these two sources, Paved2Paradise can synthesize a combinatorially large set of diverse training scenes. The pipeline involves four steps: (1) collecting extensive background LiDAR scans, (2) recording high-resolution scans of target objects under controlled conditions, (3) inserting objects into backgrounds with physically consistent placement and occlusion, and (4) simulating LiDAR geometry to ensure realism.

Experiments show that models trained on Paved2Paradise-generated data transfer effectively to the real world, achieving strong detection performance with far less manual annotation compared to conventional dataset collection. The approach is not only cost-efficient, but also flexible—allowing practitioners to easily expand to new object classes or domains by swapping in new background or object scans. For ML practitioners working in robotics, autonomous vehicles, or safety-critical perception, Paved2Paradise highlights a practical path toward scaling training data without scaling costs. It bridges the gap between simulation and real-world performance, enabling faster iteration and more reliable deployment of perception models.

About the Speaker

Michael A. Alcorn is a Senior Machine Learning Engineer at John Deere\, where he develops deep learning models for LiDAR and RGB perception in safety-critical\, real-time systems. He earned his Ph.D. in Computer Science from Auburn University\, with a dissertation on improving computer vision and spatiotemporal deep neural networks\, and also holds a Graduate Minor in Mathematics. Michael’s research has been cited by researchers at DeepMind\, Google\, Meta\, Microsoft\, and OpenAI\, among others\, and his (batter\|pitcher)2vec paper was a prize-winner at the 2018 MIT Sloan Sports Analytics Conference. He has also contributed machine learning code to scikit-learn and Apache Solr\, and his GitHub repositories—which have collectively received over 2\,100 stars—have served as starting points for research and production code at many different organizations.

MothBox: inexpensive, open-source, automated insect monitor

Dr. Andy Quitmeyer will talk about the design of an exciting new open source science tool, The Mothbox. The Mothbox is an award winning project for broad scale monitoring of insects for biodiversity. It's a low cost device developed in harsh Panamanian jungles which takes super high resolution photos to then automatically ID the levels of biodiversity in forests and agriculture. After thousands of insect observations and hundreds of deployments in Panama, Peru, Mexico, Ecuador, and the US, we are now developing a new, manufacturable version to share this important tool worldwide. We will discuss the development of this device in the jungles of Panama and its importance to studying biodiversity worldwide.

About the Speaker

Dr. Andy Quitmeyer designs new ways to interact with the natural world. He has worked with large organizations like Cartoon Network, IDEO, and the Smithsonian, taught as a tenure-track professor at the National University of Singapore, and even had his research turned into a (silly) television series called “Hacking the Wild,” distributed by Discovery Networks.

Now, he spends most of his time volunteering with smaller organizations, and recently founded the field-station makerspace, Digital Naturalism Laboratories. In the rainforest of Gamboa, Panama, Dinalab blends biological fieldwork and technological crafting with a community of local and international scientists, artists, engineers, and animal rehabilitators. He currently also advises students as an affiliate professor at the University of Washington.

Foundation Models for Visual AI in Agriculture

Foundation models have enabled a new way to address tasks, by benefitting from emerging capabilities in a zero-shot manner. In this talk I will discuss recent research on enabling visual AI in a zero-shot manner and via fine-tuning. Specifically, I will discuss joint work on RELOCATE, a simple training-free baseline designed to perform the challenging task of visual query localization in long videos.

To eliminate the need for task-specific training and efficiently handle long videos, RELOCATE leverages a region-based representation derived from pretrained vision models. I will also discuss joint work on enabling multi-modal large language models (MLLMs) to correctly answer prompts that require a holistic spatio-temporal understanding: MLLMs struggle to answer prompts that refer to 1) the entirety of an environment that an agent equipped with an MLLM can operate in; and simultaneously also refer to 2) recent actions that just happened and are encoded in a video clip.

However, such a holistic spatio-temporal understanding is important for agents operating in the real world. Our solution involves development of a dedicated data collection pipeline and fine-tuning of an MLLM equipped with projectors to improve both spatial understanding of an environment and temporal understanding of recent observations.

About the Speaker

Alex Schwing is an Associate Professor at the University of Illinois at Urbana-Champaign working with talented students on artificial intelligence, generative AI, and computer vision topics. He received his B.S. and diploma in Electrical Engineering and Information Technology from the Technical University of Munich in 2006 and 2008 respectively, and obtained a PhD in Computer Science from ETH Zurich in 2014. Afterwards he joined University of Toronto as a postdoctoral fellow until 2016.

His research interests are in the area of artificial intelligence, generative AI, and computer vision, where he has co-authored numerous papers on topics in scene understanding, inference and learning algorithms, deep learning, image and language processing, and generative modeling. His PhD thesis was awarded an ETH medal and his team’s research was awarded an NSF CAREER award.

Beyond the Lab: Real-World Anomaly Detection for Agricultural Computer Vision

Anomaly detection is transforming manufacturing and surveillance, but what about agriculture? Can AI actually detect plant diseases and pest damage early enough to make a difference? This talk demonstrates how anomaly detection identifies and localizes crop problems using coffee leaf health as our primary example. We'll start with the foundational theory, then examine how these models detect rust and miner damage in leaf imagery.

The session includes a comprehensive hands-on workflow using the open-source FiftyOne computer vision toolkit, covering dataset curation, patch extraction, model training, and result visualization. You'll gain both theoretical understanding of anomaly detection in computer vision and practical experience applying these techniques to agricultural challenges and other domains.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Oct 15 - Visual AI in Agriculture (Day 1)

Oct 15 - Visual AI in Agriculture (Day 1) 2025-10-15 · 16:00

Join us for day one of a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI in Agriculture.

Date and Time Oct 15 at 9 AM Pacific

Location Virtual. Register for the Zoom.

Paved2Paradise: Scalable LiDAR Simulation for Real-World Perception

Training robust perception models for robotics and autonomy often requires massive, diverse 3D datasets. But collecting and annotating real-world LiDAR point clouds at scale is both expensive and time-consuming, especially when high-quality labels are needed. Paved2Paradise introduces a cost-effective alternative: a scalable LiDAR simulation pipeline that generates realistic, fully annotated datasets with minimal human labeling effort.

The key idea is to “factor the real world” by separately capturing background scans (e.g., fields, roads, construction sites) and object scans (e.g., vehicles, people, machinery). By intelligently combining these two sources, Paved2Paradise can synthesize a combinatorially large set of diverse training scenes. The pipeline involves four steps: (1) collecting extensive background LiDAR scans, (2) recording high-resolution scans of target objects under controlled conditions, (3) inserting objects into backgrounds with physically consistent placement and occlusion, and (4) simulating LiDAR geometry to ensure realism.

Experiments show that models trained on Paved2Paradise-generated data transfer effectively to the real world, achieving strong detection performance with far less manual annotation compared to conventional dataset collection. The approach is not only cost-efficient, but also flexible—allowing practitioners to easily expand to new object classes or domains by swapping in new background or object scans. For ML practitioners working in robotics, autonomous vehicles, or safety-critical perception, Paved2Paradise highlights a practical path toward scaling training data without scaling costs. It bridges the gap between simulation and real-world performance, enabling faster iteration and more reliable deployment of perception models.

About the Speaker

Michael A. Alcorn is a Senior Machine Learning Engineer at John Deere\, where he develops deep learning models for LiDAR and RGB perception in safety-critical\, real-time systems. He earned his Ph.D. in Computer Science from Auburn University\, with a dissertation on improving computer vision and spatiotemporal deep neural networks\, and also holds a Graduate Minor in Mathematics. Michael’s research has been cited by researchers at DeepMind\, Google\, Meta\, Microsoft\, and OpenAI\, among others\, and his (batter\|pitcher)2vec paper was a prize-winner at the 2018 MIT Sloan Sports Analytics Conference. He has also contributed machine learning code to scikit-learn and Apache Solr\, and his GitHub repositories—which have collectively received over 2\,100 stars—have served as starting points for research and production code at many different organizations.

MothBox: inexpensive, open-source, automated insect monitor

Dr. Andy Quitmeyer will talk about the design of an exciting new open source science tool, The Mothbox. The Mothbox is an award winning project for broad scale monitoring of insects for biodiversity. It's a low cost device developed in harsh Panamanian jungles which takes super high resolution photos to then automatically ID the levels of biodiversity in forests and agriculture. After thousands of insect observations and hundreds of deployments in Panama, Peru, Mexico, Ecuador, and the US, we are now developing a new, manufacturable version to share this important tool worldwide. We will discuss the development of this device in the jungles of Panama and its importance to studying biodiversity worldwide.

About the Speaker

Dr. Andy Quitmeyer designs new ways to interact with the natural world. He has worked with large organizations like Cartoon Network, IDEO, and the Smithsonian, taught as a tenure-track professor at the National University of Singapore, and even had his research turned into a (silly) television series called “Hacking the Wild,” distributed by Discovery Networks.

Now, he spends most of his time volunteering with smaller organizations, and recently founded the field-station makerspace, Digital Naturalism Laboratories. In the rainforest of Gamboa, Panama, Dinalab blends biological fieldwork and technological crafting with a community of local and international scientists, artists, engineers, and animal rehabilitators. He currently also advises students as an affiliate professor at the University of Washington.

Foundation Models for Visual AI in Agriculture

Foundation models have enabled a new way to address tasks, by benefitting from emerging capabilities in a zero-shot manner. In this talk I will discuss recent research on enabling visual AI in a zero-shot manner and via fine-tuning. Specifically, I will discuss joint work on RELOCATE, a simple training-free baseline designed to perform the challenging task of visual query localization in long videos.

To eliminate the need for task-specific training and efficiently handle long videos, RELOCATE leverages a region-based representation derived from pretrained vision models. I will also discuss joint work on enabling multi-modal large language models (MLLMs) to correctly answer prompts that require a holistic spatio-temporal understanding: MLLMs struggle to answer prompts that refer to 1) the entirety of an environment that an agent equipped with an MLLM can operate in; and simultaneously also refer to 2) recent actions that just happened and are encoded in a video clip.

However, such a holistic spatio-temporal understanding is important for agents operating in the real world. Our solution involves development of a dedicated data collection pipeline and fine-tuning of an MLLM equipped with projectors to improve both spatial understanding of an environment and temporal understanding of recent observations.

About the Speaker

Alex Schwing is an Associate Professor at the University of Illinois at Urbana-Champaign working with talented students on artificial intelligence, generative AI, and computer vision topics. He received his B.S. and diploma in Electrical Engineering and Information Technology from the Technical University of Munich in 2006 and 2008 respectively, and obtained a PhD in Computer Science from ETH Zurich in 2014. Afterwards he joined University of Toronto as a postdoctoral fellow until 2016.

His research interests are in the area of artificial intelligence, generative AI, and computer vision, where he has co-authored numerous papers on topics in scene understanding, inference and learning algorithms, deep learning, image and language processing, and generative modeling. His PhD thesis was awarded an ETH medal and his team’s research was awarded an NSF CAREER award.

Beyond the Lab: Real-World Anomaly Detection for Agricultural Computer Vision

Anomaly detection is transforming manufacturing and surveillance, but what about agriculture? Can AI actually detect plant diseases and pest damage early enough to make a difference? This talk demonstrates how anomaly detection identifies and localizes crop problems using coffee leaf health as our primary example. We'll start with the foundational theory, then examine how these models detect rust and miner damage in leaf imagery.

The session includes a comprehensive hands-on workflow using the open-source FiftyOne computer vision toolkit, covering dataset curation, patch extraction, model training, and result visualization. You'll gain both theoretical understanding of anomaly detection in computer vision and practical experience applying these techniques to agricultural challenges and other domains.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Oct 15 - Visual AI in Agriculture (Day 1)

Oct 15 - Visual AI in Agriculture (Day 1) 2025-10-15 · 16:00

Join us for day one of a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI in Agriculture.

Date and Time Oct 15 at 9 AM Pacific

Location Virtual. Register for the Zoom.

Paved2Paradise: Scalable LiDAR Simulation for Real-World Perception

Training robust perception models for robotics and autonomy often requires massive, diverse 3D datasets. But collecting and annotating real-world LiDAR point clouds at scale is both expensive and time-consuming, especially when high-quality labels are needed. Paved2Paradise introduces a cost-effective alternative: a scalable LiDAR simulation pipeline that generates realistic, fully annotated datasets with minimal human labeling effort.

The key idea is to “factor the real world” by separately capturing background scans (e.g., fields, roads, construction sites) and object scans (e.g., vehicles, people, machinery). By intelligently combining these two sources, Paved2Paradise can synthesize a combinatorially large set of diverse training scenes. The pipeline involves four steps: (1) collecting extensive background LiDAR scans, (2) recording high-resolution scans of target objects under controlled conditions, (3) inserting objects into backgrounds with physically consistent placement and occlusion, and (4) simulating LiDAR geometry to ensure realism.

Experiments show that models trained on Paved2Paradise-generated data transfer effectively to the real world, achieving strong detection performance with far less manual annotation compared to conventional dataset collection. The approach is not only cost-efficient, but also flexible—allowing practitioners to easily expand to new object classes or domains by swapping in new background or object scans. For ML practitioners working in robotics, autonomous vehicles, or safety-critical perception, Paved2Paradise highlights a practical path toward scaling training data without scaling costs. It bridges the gap between simulation and real-world performance, enabling faster iteration and more reliable deployment of perception models.

About the Speaker

Michael A. Alcorn is a Senior Machine Learning Engineer at John Deere\, where he develops deep learning models for LiDAR and RGB perception in safety-critical\, real-time systems. He earned his Ph.D. in Computer Science from Auburn University\, with a dissertation on improving computer vision and spatiotemporal deep neural networks\, and also holds a Graduate Minor in Mathematics. Michael’s research has been cited by researchers at DeepMind\, Google\, Meta\, Microsoft\, and OpenAI\, among others\, and his (batter\|pitcher)2vec paper was a prize-winner at the 2018 MIT Sloan Sports Analytics Conference. He has also contributed machine learning code to scikit-learn and Apache Solr\, and his GitHub repositories—which have collectively received over 2\,100 stars—have served as starting points for research and production code at many different organizations.

MothBox: inexpensive, open-source, automated insect monitor

Dr. Andy Quitmeyer will talk about the design of an exciting new open source science tool, The Mothbox. The Mothbox is an award winning project for broad scale monitoring of insects for biodiversity. It's a low cost device developed in harsh Panamanian jungles which takes super high resolution photos to then automatically ID the levels of biodiversity in forests and agriculture. After thousands of insect observations and hundreds of deployments in Panama, Peru, Mexico, Ecuador, and the US, we are now developing a new, manufacturable version to share this important tool worldwide. We will discuss the development of this device in the jungles of Panama and its importance to studying biodiversity worldwide.

About the Speaker

Dr. Andy Quitmeyer designs new ways to interact with the natural world. He has worked with large organizations like Cartoon Network, IDEO, and the Smithsonian, taught as a tenure-track professor at the National University of Singapore, and even had his research turned into a (silly) television series called “Hacking the Wild,” distributed by Discovery Networks.

Now, he spends most of his time volunteering with smaller organizations, and recently founded the field-station makerspace, Digital Naturalism Laboratories. In the rainforest of Gamboa, Panama, Dinalab blends biological fieldwork and technological crafting with a community of local and international scientists, artists, engineers, and animal rehabilitators. He currently also advises students as an affiliate professor at the University of Washington.

Foundation Models for Visual AI in Agriculture

Foundation models have enabled a new way to address tasks, by benefitting from emerging capabilities in a zero-shot manner. In this talk I will discuss recent research on enabling visual AI in a zero-shot manner and via fine-tuning. Specifically, I will discuss joint work on RELOCATE, a simple training-free baseline designed to perform the challenging task of visual query localization in long videos.

To eliminate the need for task-specific training and efficiently handle long videos, RELOCATE leverages a region-based representation derived from pretrained vision models. I will also discuss joint work on enabling multi-modal large language models (MLLMs) to correctly answer prompts that require a holistic spatio-temporal understanding: MLLMs struggle to answer prompts that refer to 1) the entirety of an environment that an agent equipped with an MLLM can operate in; and simultaneously also refer to 2) recent actions that just happened and are encoded in a video clip.

However, such a holistic spatio-temporal understanding is important for agents operating in the real world. Our solution involves development of a dedicated data collection pipeline and fine-tuning of an MLLM equipped with projectors to improve both spatial understanding of an environment and temporal understanding of recent observations.

About the Speaker

Alex Schwing is an Associate Professor at the University of Illinois at Urbana-Champaign working with talented students on artificial intelligence, generative AI, and computer vision topics. He received his B.S. and diploma in Electrical Engineering and Information Technology from the Technical University of Munich in 2006 and 2008 respectively, and obtained a PhD in Computer Science from ETH Zurich in 2014. Afterwards he joined University of Toronto as a postdoctoral fellow until 2016.

His research interests are in the area of artificial intelligence, generative AI, and computer vision, where he has co-authored numerous papers on topics in scene understanding, inference and learning algorithms, deep learning, image and language processing, and generative modeling. His PhD thesis was awarded an ETH medal and his team’s research was awarded an NSF CAREER award.

Beyond the Lab: Real-World Anomaly Detection for Agricultural Computer Vision

Anomaly detection is transforming manufacturing and surveillance, but what about agriculture? Can AI actually detect plant diseases and pest damage early enough to make a difference? This talk demonstrates how anomaly detection identifies and localizes crop problems using coffee leaf health as our primary example. We'll start with the foundational theory, then examine how these models detect rust and miner damage in leaf imagery.

The session includes a comprehensive hands-on workflow using the open-source FiftyOne computer vision toolkit, covering dataset curation, patch extraction, model training, and result visualization. You'll gain both theoretical understanding of anomaly detection in computer vision and practical experience applying these techniques to agricultural challenges and other domains.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Oct 15 - Visual AI in Agriculture (Day 1)

Oct 15 - Visual AI in Agriculture (Day 1) 2025-10-15 · 16:00

Join us for day one of a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI in Agriculture.

Date and Time Oct 15 at 9 AM Pacific

Location Virtual. Register for the Zoom.

Paved2Paradise: Scalable LiDAR Simulation for Real-World Perception

Training robust perception models for robotics and autonomy often requires massive, diverse 3D datasets. But collecting and annotating real-world LiDAR point clouds at scale is both expensive and time-consuming, especially when high-quality labels are needed. Paved2Paradise introduces a cost-effective alternative: a scalable LiDAR simulation pipeline that generates realistic, fully annotated datasets with minimal human labeling effort.

The key idea is to “factor the real world” by separately capturing background scans (e.g., fields, roads, construction sites) and object scans (e.g., vehicles, people, machinery). By intelligently combining these two sources, Paved2Paradise can synthesize a combinatorially large set of diverse training scenes. The pipeline involves four steps: (1) collecting extensive background LiDAR scans, (2) recording high-resolution scans of target objects under controlled conditions, (3) inserting objects into backgrounds with physically consistent placement and occlusion, and (4) simulating LiDAR geometry to ensure realism.

Experiments show that models trained on Paved2Paradise-generated data transfer effectively to the real world, achieving strong detection performance with far less manual annotation compared to conventional dataset collection. The approach is not only cost-efficient, but also flexible—allowing practitioners to easily expand to new object classes or domains by swapping in new background or object scans. For ML practitioners working in robotics, autonomous vehicles, or safety-critical perception, Paved2Paradise highlights a practical path toward scaling training data without scaling costs. It bridges the gap between simulation and real-world performance, enabling faster iteration and more reliable deployment of perception models.

About the Speaker

Michael A. Alcorn is a Senior Machine Learning Engineer at John Deere\, where he develops deep learning models for LiDAR and RGB perception in safety-critical\, real-time systems. He earned his Ph.D. in Computer Science from Auburn University\, with a dissertation on improving computer vision and spatiotemporal deep neural networks\, and also holds a Graduate Minor in Mathematics. Michael’s research has been cited by researchers at DeepMind\, Google\, Meta\, Microsoft\, and OpenAI\, among others\, and his (batter\|pitcher)2vec paper was a prize-winner at the 2018 MIT Sloan Sports Analytics Conference. He has also contributed machine learning code to scikit-learn and Apache Solr\, and his GitHub repositories—which have collectively received over 2\,100 stars—have served as starting points for research and production code at many different organizations.

MothBox: inexpensive, open-source, automated insect monitor

Dr. Andy Quitmeyer will talk about the design of an exciting new open source science tool, The Mothbox. The Mothbox is an award winning project for broad scale monitoring of insects for biodiversity. It's a low cost device developed in harsh Panamanian jungles which takes super high resolution photos to then automatically ID the levels of biodiversity in forests and agriculture. After thousands of insect observations and hundreds of deployments in Panama, Peru, Mexico, Ecuador, and the US, we are now developing a new, manufacturable version to share this important tool worldwide. We will discuss the development of this device in the jungles of Panama and its importance to studying biodiversity worldwide.

About the Speaker

Dr. Andy Quitmeyer designs new ways to interact with the natural world. He has worked with large organizations like Cartoon Network, IDEO, and the Smithsonian, taught as a tenure-track professor at the National University of Singapore, and even had his research turned into a (silly) television series called “Hacking the Wild,” distributed by Discovery Networks.

Now, he spends most of his time volunteering with smaller organizations, and recently founded the field-station makerspace, Digital Naturalism Laboratories. In the rainforest of Gamboa, Panama, Dinalab blends biological fieldwork and technological crafting with a community of local and international scientists, artists, engineers, and animal rehabilitators. He currently also advises students as an affiliate professor at the University of Washington.

Foundation Models for Visual AI in Agriculture

Foundation models have enabled a new way to address tasks, by benefitting from emerging capabilities in a zero-shot manner. In this talk I will discuss recent research on enabling visual AI in a zero-shot manner and via fine-tuning. Specifically, I will discuss joint work on RELOCATE, a simple training-free baseline designed to perform the challenging task of visual query localization in long videos.

To eliminate the need for task-specific training and efficiently handle long videos, RELOCATE leverages a region-based representation derived from pretrained vision models. I will also discuss joint work on enabling multi-modal large language models (MLLMs) to correctly answer prompts that require a holistic spatio-temporal understanding: MLLMs struggle to answer prompts that refer to 1) the entirety of an environment that an agent equipped with an MLLM can operate in; and simultaneously also refer to 2) recent actions that just happened and are encoded in a video clip.

However, such a holistic spatio-temporal understanding is important for agents operating in the real world. Our solution involves development of a dedicated data collection pipeline and fine-tuning of an MLLM equipped with projectors to improve both spatial understanding of an environment and temporal understanding of recent observations.

About the Speaker

Alex Schwing is an Associate Professor at the University of Illinois at Urbana-Champaign working with talented students on artificial intelligence, generative AI, and computer vision topics. He received his B.S. and diploma in Electrical Engineering and Information Technology from the Technical University of Munich in 2006 and 2008 respectively, and obtained a PhD in Computer Science from ETH Zurich in 2014. Afterwards he joined University of Toronto as a postdoctoral fellow until 2016.

His research interests are in the area of artificial intelligence, generative AI, and computer vision, where he has co-authored numerous papers on topics in scene understanding, inference and learning algorithms, deep learning, image and language processing, and generative modeling. His PhD thesis was awarded an ETH medal and his team’s research was awarded an NSF CAREER award.

Beyond the Lab: Real-World Anomaly Detection for Agricultural Computer Vision

Anomaly detection is transforming manufacturing and surveillance, but what about agriculture? Can AI actually detect plant diseases and pest damage early enough to make a difference? This talk demonstrates how anomaly detection identifies and localizes crop problems using coffee leaf health as our primary example. We'll start with the foundational theory, then examine how these models detect rust and miner damage in leaf imagery.

The session includes a comprehensive hands-on workflow using the open-source FiftyOne computer vision toolkit, covering dataset curation, patch extraction, model training, and result visualization. You'll gain both theoretical understanding of anomaly detection in computer vision and practical experience applying these techniques to agricultural challenges and other domains.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Oct 15 - Visual AI in Agriculture (Day 1)

Oct 15 - Visual AI in Agriculture (Day 1) 2025-10-15 · 16:00

Join us for day one of a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI in Agriculture.

Date and Time Oct 15 at 9 AM Pacific

Location Virtual. Register for the Zoom.

Paved2Paradise: Scalable LiDAR Simulation for Real-World Perception

Training robust perception models for robotics and autonomy often requires massive, diverse 3D datasets. But collecting and annotating real-world LiDAR point clouds at scale is both expensive and time-consuming, especially when high-quality labels are needed. Paved2Paradise introduces a cost-effective alternative: a scalable LiDAR simulation pipeline that generates realistic, fully annotated datasets with minimal human labeling effort.

The key idea is to “factor the real world” by separately capturing background scans (e.g., fields, roads, construction sites) and object scans (e.g., vehicles, people, machinery). By intelligently combining these two sources, Paved2Paradise can synthesize a combinatorially large set of diverse training scenes. The pipeline involves four steps: (1) collecting extensive background LiDAR scans, (2) recording high-resolution scans of target objects under controlled conditions, (3) inserting objects into backgrounds with physically consistent placement and occlusion, and (4) simulating LiDAR geometry to ensure realism.

Experiments show that models trained on Paved2Paradise-generated data transfer effectively to the real world, achieving strong detection performance with far less manual annotation compared to conventional dataset collection. The approach is not only cost-efficient, but also flexible—allowing practitioners to easily expand to new object classes or domains by swapping in new background or object scans. For ML practitioners working in robotics, autonomous vehicles, or safety-critical perception, Paved2Paradise highlights a practical path toward scaling training data without scaling costs. It bridges the gap between simulation and real-world performance, enabling faster iteration and more reliable deployment of perception models.

About the Speaker

Michael A. Alcorn is a Senior Machine Learning Engineer at John Deere\, where he develops deep learning models for LiDAR and RGB perception in safety-critical\, real-time systems. He earned his Ph.D. in Computer Science from Auburn University\, with a dissertation on improving computer vision and spatiotemporal deep neural networks\, and also holds a Graduate Minor in Mathematics. Michael’s research has been cited by researchers at DeepMind\, Google\, Meta\, Microsoft\, and OpenAI\, among others\, and his (batter\|pitcher)2vec paper was a prize-winner at the 2018 MIT Sloan Sports Analytics Conference. He has also contributed machine learning code to scikit-learn and Apache Solr\, and his GitHub repositories—which have collectively received over 2\,100 stars—have served as starting points for research and production code at many different organizations.

MothBox: inexpensive, open-source, automated insect monitor

Dr. Andy Quitmeyer will talk about the design of an exciting new open source science tool, The Mothbox. The Mothbox is an award winning project for broad scale monitoring of insects for biodiversity. It's a low cost device developed in harsh Panamanian jungles which takes super high resolution photos to then automatically ID the levels of biodiversity in forests and agriculture. After thousands of insect observations and hundreds of deployments in Panama, Peru, Mexico, Ecuador, and the US, we are now developing a new, manufacturable version to share this important tool worldwide. We will discuss the development of this device in the jungles of Panama and its importance to studying biodiversity worldwide.

About the Speaker

Dr. Andy Quitmeyer designs new ways to interact with the natural world. He has worked with large organizations like Cartoon Network, IDEO, and the Smithsonian, taught as a tenure-track professor at the National University of Singapore, and even had his research turned into a (silly) television series called “Hacking the Wild,” distributed by Discovery Networks.

Now, he spends most of his time volunteering with smaller organizations, and recently founded the field-station makerspace, Digital Naturalism Laboratories. In the rainforest of Gamboa, Panama, Dinalab blends biological fieldwork and technological crafting with a community of local and international scientists, artists, engineers, and animal rehabilitators. He currently also advises students as an affiliate professor at the University of Washington.

Foundation Models for Visual AI in Agriculture

Foundation models have enabled a new way to address tasks, by benefitting from emerging capabilities in a zero-shot manner. In this talk I will discuss recent research on enabling visual AI in a zero-shot manner and via fine-tuning. Specifically, I will discuss joint work on RELOCATE, a simple training-free baseline designed to perform the challenging task of visual query localization in long videos.

To eliminate the need for task-specific training and efficiently handle long videos, RELOCATE leverages a region-based representation derived from pretrained vision models. I will also discuss joint work on enabling multi-modal large language models (MLLMs) to correctly answer prompts that require a holistic spatio-temporal understanding: MLLMs struggle to answer prompts that refer to 1) the entirety of an environment that an agent equipped with an MLLM can operate in; and simultaneously also refer to 2) recent actions that just happened and are encoded in a video clip.

However, such a holistic spatio-temporal understanding is important for agents operating in the real world. Our solution involves development of a dedicated data collection pipeline and fine-tuning of an MLLM equipped with projectors to improve both spatial understanding of an environment and temporal understanding of recent observations.

About the Speaker

Alex Schwing is an Associate Professor at the University of Illinois at Urbana-Champaign working with talented students on artificial intelligence, generative AI, and computer vision topics. He received his B.S. and diploma in Electrical Engineering and Information Technology from the Technical University of Munich in 2006 and 2008 respectively, and obtained a PhD in Computer Science from ETH Zurich in 2014. Afterwards he joined University of Toronto as a postdoctoral fellow until 2016.

His research interests are in the area of artificial intelligence, generative AI, and computer vision, where he has co-authored numerous papers on topics in scene understanding, inference and learning algorithms, deep learning, image and language processing, and generative modeling. His PhD thesis was awarded an ETH medal and his team’s research was awarded an NSF CAREER award.

Beyond the Lab: Real-World Anomaly Detection for Agricultural Computer Vision

Anomaly detection is transforming manufacturing and surveillance, but what about agriculture? Can AI actually detect plant diseases and pest damage early enough to make a difference? This talk demonstrates how anomaly detection identifies and localizes crop problems using coffee leaf health as our primary example. We'll start with the foundational theory, then examine how these models detect rust and miner damage in leaf imagery.

The session includes a comprehensive hands-on workflow using the open-source FiftyOne computer vision toolkit, covering dataset curation, patch extraction, model training, and result visualization. You'll gain both theoretical understanding of anomaly detection in computer vision and practical experience applying these techniques to agricultural challenges and other domains.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Oct 15 - Visual AI in Agriculture (Day 1)

Oct 15 - Visual AI in Agriculture (Day 1) 2025-10-15 · 16:00

Join us for day one of a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI in Agriculture.

Date and Time Oct 15 at 9 AM Pacific

Location Virtual. Register for the Zoom.

Paved2Paradise: Scalable LiDAR Simulation for Real-World Perception

Training robust perception models for robotics and autonomy often requires massive, diverse 3D datasets. But collecting and annotating real-world LiDAR point clouds at scale is both expensive and time-consuming, especially when high-quality labels are needed. Paved2Paradise introduces a cost-effective alternative: a scalable LiDAR simulation pipeline that generates realistic, fully annotated datasets with minimal human labeling effort.

The key idea is to “factor the real world” by separately capturing background scans (e.g., fields, roads, construction sites) and object scans (e.g., vehicles, people, machinery). By intelligently combining these two sources, Paved2Paradise can synthesize a combinatorially large set of diverse training scenes. The pipeline involves four steps: (1) collecting extensive background LiDAR scans, (2) recording high-resolution scans of target objects under controlled conditions, (3) inserting objects into backgrounds with physically consistent placement and occlusion, and (4) simulating LiDAR geometry to ensure realism.

Experiments show that models trained on Paved2Paradise-generated data transfer effectively to the real world, achieving strong detection performance with far less manual annotation compared to conventional dataset collection. The approach is not only cost-efficient, but also flexible—allowing practitioners to easily expand to new object classes or domains by swapping in new background or object scans. For ML practitioners working in robotics, autonomous vehicles, or safety-critical perception, Paved2Paradise highlights a practical path toward scaling training data without scaling costs. It bridges the gap between simulation and real-world performance, enabling faster iteration and more reliable deployment of perception models.

About the Speaker

Michael A. Alcorn is a Senior Machine Learning Engineer at John Deere\, where he develops deep learning models for LiDAR and RGB perception in safety-critical\, real-time systems. He earned his Ph.D. in Computer Science from Auburn University\, with a dissertation on improving computer vision and spatiotemporal deep neural networks\, and also holds a Graduate Minor in Mathematics. Michael’s research has been cited by researchers at DeepMind\, Google\, Meta\, Microsoft\, and OpenAI\, among others\, and his (batter\|pitcher)2vec paper was a prize-winner at the 2018 MIT Sloan Sports Analytics Conference. He has also contributed machine learning code to scikit-learn and Apache Solr\, and his GitHub repositories—which have collectively received over 2\,100 stars—have served as starting points for research and production code at many different organizations.

MothBox: inexpensive, open-source, automated insect monitor

Dr. Andy Quitmeyer will talk about the design of an exciting new open source science tool, The Mothbox. The Mothbox is an award winning project for broad scale monitoring of insects for biodiversity. It's a low cost device developed in harsh Panamanian jungles which takes super high resolution photos to then automatically ID the levels of biodiversity in forests and agriculture. After thousands of insect observations and hundreds of deployments in Panama, Peru, Mexico, Ecuador, and the US, we are now developing a new, manufacturable version to share this important tool worldwide. We will discuss the development of this device in the jungles of Panama and its importance to studying biodiversity worldwide.

About the Speaker

Dr. Andy Quitmeyer designs new ways to interact with the natural world. He has worked with large organizations like Cartoon Network, IDEO, and the Smithsonian, taught as a tenure-track professor at the National University of Singapore, and even had his research turned into a (silly) television series called “Hacking the Wild,” distributed by Discovery Networks.

Now, he spends most of his time volunteering with smaller organizations, and recently founded the field-station makerspace, Digital Naturalism Laboratories. In the rainforest of Gamboa, Panama, Dinalab blends biological fieldwork and technological crafting with a community of local and international scientists, artists, engineers, and animal rehabilitators. He currently also advises students as an affiliate professor at the University of Washington.

Foundation Models for Visual AI in Agriculture

Foundation models have enabled a new way to address tasks, by benefitting from emerging capabilities in a zero-shot manner. In this talk I will discuss recent research on enabling visual AI in a zero-shot manner and via fine-tuning. Specifically, I will discuss joint work on RELOCATE, a simple training-free baseline designed to perform the challenging task of visual query localization in long videos.

To eliminate the need for task-specific training and efficiently handle long videos, RELOCATE leverages a region-based representation derived from pretrained vision models. I will also discuss joint work on enabling multi-modal large language models (MLLMs) to correctly answer prompts that require a holistic spatio-temporal understanding: MLLMs struggle to answer prompts that refer to 1) the entirety of an environment that an agent equipped with an MLLM can operate in; and simultaneously also refer to 2) recent actions that just happened and are encoded in a video clip.

However, such a holistic spatio-temporal understanding is important for agents operating in the real world. Our solution involves development of a dedicated data collection pipeline and fine-tuning of an MLLM equipped with projectors to improve both spatial understanding of an environment and temporal understanding of recent observations.

About the Speaker

Alex Schwing is an Associate Professor at the University of Illinois at Urbana-Champaign working with talented students on artificial intelligence, generative AI, and computer vision topics. He received his B.S. and diploma in Electrical Engineering and Information Technology from the Technical University of Munich in 2006 and 2008 respectively, and obtained a PhD in Computer Science from ETH Zurich in 2014. Afterwards he joined University of Toronto as a postdoctoral fellow until 2016.

His research interests are in the area of artificial intelligence, generative AI, and computer vision, where he has co-authored numerous papers on topics in scene understanding, inference and learning algorithms, deep learning, image and language processing, and generative modeling. His PhD thesis was awarded an ETH medal and his team’s research was awarded an NSF CAREER award.

Beyond the Lab: Real-World Anomaly Detection for Agricultural Computer Vision

Anomaly detection is transforming manufacturing and surveillance, but what about agriculture? Can AI actually detect plant diseases and pest damage early enough to make a difference? This talk demonstrates how anomaly detection identifies and localizes crop problems using coffee leaf health as our primary example. We'll start with the foundational theory, then examine how these models detect rust and miner damage in leaf imagery.

The session includes a comprehensive hands-on workflow using the open-source FiftyOne computer vision toolkit, covering dataset curation, patch extraction, model training, and result visualization. You'll gain both theoretical understanding of anomaly detection in computer vision and practical experience applying these techniques to agricultural challenges and other domains.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Oct 15 - Visual AI in Agriculture (Day 1)

Oct 15 - Visual AI in Agriculture (Day 1) 2025-10-15 · 16:00

Join us for day one of a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI in Agriculture.

Date and Time Oct 15 at 9 AM Pacific

Location Virtual. Register for the Zoom.

Paved2Paradise: Scalable LiDAR Simulation for Real-World Perception

Training robust perception models for robotics and autonomy often requires massive, diverse 3D datasets. But collecting and annotating real-world LiDAR point clouds at scale is both expensive and time-consuming, especially when high-quality labels are needed. Paved2Paradise introduces a cost-effective alternative: a scalable LiDAR simulation pipeline that generates realistic, fully annotated datasets with minimal human labeling effort.

The key idea is to “factor the real world” by separately capturing background scans (e.g., fields, roads, construction sites) and object scans (e.g., vehicles, people, machinery). By intelligently combining these two sources, Paved2Paradise can synthesize a combinatorially large set of diverse training scenes. The pipeline involves four steps: (1) collecting extensive background LiDAR scans, (2) recording high-resolution scans of target objects under controlled conditions, (3) inserting objects into backgrounds with physically consistent placement and occlusion, and (4) simulating LiDAR geometry to ensure realism.

Experiments show that models trained on Paved2Paradise-generated data transfer effectively to the real world, achieving strong detection performance with far less manual annotation compared to conventional dataset collection. The approach is not only cost-efficient, but also flexible—allowing practitioners to easily expand to new object classes or domains by swapping in new background or object scans. For ML practitioners working in robotics, autonomous vehicles, or safety-critical perception, Paved2Paradise highlights a practical path toward scaling training data without scaling costs. It bridges the gap between simulation and real-world performance, enabling faster iteration and more reliable deployment of perception models.

About the Speaker

Michael A. Alcorn is a Senior Machine Learning Engineer at John Deere\, where he develops deep learning models for LiDAR and RGB perception in safety-critical\, real-time systems. He earned his Ph.D. in Computer Science from Auburn University\, with a dissertation on improving computer vision and spatiotemporal deep neural networks\, and also holds a Graduate Minor in Mathematics. Michael’s research has been cited by researchers at DeepMind\, Google\, Meta\, Microsoft\, and OpenAI\, among others\, and his (batter\|pitcher)2vec paper was a prize-winner at the 2018 MIT Sloan Sports Analytics Conference. He has also contributed machine learning code to scikit-learn and Apache Solr\, and his GitHub repositories—which have collectively received over 2\,100 stars—have served as starting points for research and production code at many different organizations.

MothBox: inexpensive, open-source, automated insect monitor

Dr. Andy Quitmeyer will talk about the design of an exciting new open source science tool, The Mothbox. The Mothbox is an award winning project for broad scale monitoring of insects for biodiversity. It's a low cost device developed in harsh Panamanian jungles which takes super high resolution photos to then automatically ID the levels of biodiversity in forests and agriculture. After thousands of insect observations and hundreds of deployments in Panama, Peru, Mexico, Ecuador, and the US, we are now developing a new, manufacturable version to share this important tool worldwide. We will discuss the development of this device in the jungles of Panama and its importance to studying biodiversity worldwide.

About the Speaker

Dr. Andy Quitmeyer designs new ways to interact with the natural world. He has worked with large organizations like Cartoon Network, IDEO, and the Smithsonian, taught as a tenure-track professor at the National University of Singapore, and even had his research turned into a (silly) television series called “Hacking the Wild,” distributed by Discovery Networks.

Now, he spends most of his time volunteering with smaller organizations, and recently founded the field-station makerspace, Digital Naturalism Laboratories. In the rainforest of Gamboa, Panama, Dinalab blends biological fieldwork and technological crafting with a community of local and international scientists, artists, engineers, and animal rehabilitators. He currently also advises students as an affiliate professor at the University of Washington.

Foundation Models for Visual AI in Agriculture

Foundation models have enabled a new way to address tasks, by benefitting from emerging capabilities in a zero-shot manner. In this talk I will discuss recent research on enabling visual AI in a zero-shot manner and via fine-tuning. Specifically, I will discuss joint work on RELOCATE, a simple training-free baseline designed to perform the challenging task of visual query localization in long videos.

To eliminate the need for task-specific training and efficiently handle long videos, RELOCATE leverages a region-based representation derived from pretrained vision models. I will also discuss joint work on enabling multi-modal large language models (MLLMs) to correctly answer prompts that require a holistic spatio-temporal understanding: MLLMs struggle to answer prompts that refer to 1) the entirety of an environment that an agent equipped with an MLLM can operate in; and simultaneously also refer to 2) recent actions that just happened and are encoded in a video clip.

However, such a holistic spatio-temporal understanding is important for agents operating in the real world. Our solution involves development of a dedicated data collection pipeline and fine-tuning of an MLLM equipped with projectors to improve both spatial understanding of an environment and temporal understanding of recent observations.

About the Speaker

Alex Schwing is an Associate Professor at the University of Illinois at Urbana-Champaign working with talented students on artificial intelligence, generative AI, and computer vision topics. He received his B.S. and diploma in Electrical Engineering and Information Technology from the Technical University of Munich in 2006 and 2008 respectively, and obtained a PhD in Computer Science from ETH Zurich in 2014. Afterwards he joined University of Toronto as a postdoctoral fellow until 2016.

His research interests are in the area of artificial intelligence, generative AI, and computer vision, where he has co-authored numerous papers on topics in scene understanding, inference and learning algorithms, deep learning, image and language processing, and generative modeling. His PhD thesis was awarded an ETH medal and his team’s research was awarded an NSF CAREER award.

Beyond the Lab: Real-World Anomaly Detection for Agricultural Computer Vision

Anomaly detection is transforming manufacturing and surveillance, but what about agriculture? Can AI actually detect plant diseases and pest damage early enough to make a difference? This talk demonstrates how anomaly detection identifies and localizes crop problems using coffee leaf health as our primary example. We'll start with the foundational theory, then examine how these models detect rust and miner damage in leaf imagery.

The session includes a comprehensive hands-on workflow using the open-source FiftyOne computer vision toolkit, covering dataset curation, patch extraction, model training, and result visualization. You'll gain both theoretical understanding of anomaly detection in computer vision and practical experience applying these techniques to agricultural challenges and other domains.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Oct 15 - Visual AI in Agriculture (Day 1)

Oct 2 - Women in AI Virtual Event 2025-10-02 · 16:00

Hear talks from experts on the latest topics in AI, ML, and computer vision.

Date and Time

Oct 2 at 9 AM Pacific

Location

Virtual. Register for the Zoom.

The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI

As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems.

About the Speaker

Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones.

Managing Medical Imaging Datasets: From Curation to Evaluation

High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment.

We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities.

Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Building Agents That Learn: Managing Memory in AI Agents

In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time.

About the Speaker

Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops.

Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing

Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments.

About the Speaker

Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.

Oct 2 - Women in AI Virtual Event

Oct 2 - Women in AI Virtual Event 2025-10-02 · 16:00

Hear talks from experts on the latest topics in AI, ML, and computer vision.

Date and Time

Oct 2 at 9 AM Pacific

Location

Virtual. Register for the Zoom.

The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI

As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems.

About the Speaker

Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones.

Managing Medical Imaging Datasets: From Curation to Evaluation

High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment.

We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities.

Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Building Agents That Learn: Managing Memory in AI Agents

In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time.

About the Speaker

Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops.

Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing

Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments.

About the Speaker

Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.

Oct 2 - Women in AI Virtual Event

Oct 2 - Women in AI Virtual Event 2025-10-02 · 16:00

Hear talks from experts on the latest topics in AI, ML, and computer vision.

Date and Time

Oct 2 at 9 AM Pacific

Location

Virtual. Register for the Zoom.

The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI

As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems.

About the Speaker

Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones.

Managing Medical Imaging Datasets: From Curation to Evaluation

High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment.

We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities.

Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Building Agents That Learn: Managing Memory in AI Agents

In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time.

About the Speaker

Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops.

Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing

Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments.

About the Speaker

Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.

Oct 2 - Women in AI Virtual Event

Oct 2 - Women in AI Virtual Event 2025-10-02 · 16:00

Hear talks from experts on the latest topics in AI, ML, and computer vision.

Date and Time

Oct 2 at 9 AM Pacific

Location

Virtual. Register for the Zoom.

The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI

As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems.

About the Speaker

Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones.

Managing Medical Imaging Datasets: From Curation to Evaluation

High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment.

We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities.

Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Building Agents That Learn: Managing Memory in AI Agents

In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time.

About the Speaker

Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops.

Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing

Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments.

About the Speaker

Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.

Oct 2 - Women in AI Virtual Event

Oct 2 - Women in AI Virtual Event 2025-10-02 · 16:00

Hear talks from experts on the latest topics in AI, ML, and computer vision.

Date and Time

Oct 2 at 9 AM Pacific

Location

Virtual. Register for the Zoom.

The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI

As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems.

About the Speaker

Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones.

Managing Medical Imaging Datasets: From Curation to Evaluation

High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment.

We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities.

Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Building Agents That Learn: Managing Memory in AI Agents

In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time.

About the Speaker

Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops.

Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing

Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments.

About the Speaker

Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.

Oct 2 - Women in AI Virtual Event

Oct 2 - Women in AI Virtual Event 2025-10-02 · 16:00

Hear talks from experts on the latest topics in AI, ML, and computer vision.

Date and Time

Oct 2 at 9 AM Pacific

Location

Virtual. Register for the Zoom.

The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI

As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems.

About the Speaker

Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones.

Managing Medical Imaging Datasets: From Curation to Evaluation

High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment.

We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities.

Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Building Agents That Learn: Managing Memory in AI Agents

In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time.

About the Speaker

Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops.

Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing

Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments.

About the Speaker

Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.

Oct 2 - Women in AI Virtual Event

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