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Jan 22 - Women in AI
2026-01-22 · 23:00
Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd. Date, Time and Location Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom! Align Before You Recommend The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements. While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning. By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items. Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage About the Speaker Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts. Generalizable Vision-Language Models: Challenges, Advances, and Future Directions Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings. About the Speaker Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM. Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved! About the Speaker Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist. FiftyOne Labs: Enabling experimentation for the computer vision community FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product. About the Speaker Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data. |
Jan 22 - Women in AI
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Jan 22 - Women in AI
2026-01-22 · 23:00
Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd. Date, Time and Location Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom! Align Before You Recommend The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements. While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning. By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items. Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage About the Speaker Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts. Generalizable Vision-Language Models: Challenges, Advances, and Future Directions Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings. About the Speaker Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM. Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved! About the Speaker Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist. FiftyOne Labs: Enabling experimentation for the computer vision community FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product. About the Speaker Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data. |
Jan 22 - Women in AI
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Jan 22 - Women in AI
2026-01-22 · 23:00
Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd. Date, Time and Location Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom! Align Before You Recommend The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements. While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning. By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items. Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage About the Speaker Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts. Generalizable Vision-Language Models: Challenges, Advances, and Future Directions Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings. About the Speaker Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM. Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved! About the Speaker Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist. FiftyOne Labs: Enabling experimentation for the computer vision community FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product. About the Speaker Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data. |
Jan 22 - Women in AI
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Jan 22 - Women in AI
2026-01-22 · 23:00
Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd. Date, Time and Location Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom! Align Before You Recommend The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements. While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning. By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items. Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage About the Speaker Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts. Generalizable Vision-Language Models: Challenges, Advances, and Future Directions Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings. About the Speaker Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM. Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved! About the Speaker Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist. FiftyOne Labs: Enabling experimentation for the computer vision community FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product. About the Speaker Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data. |
Jan 22 - Women in AI
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Jan 22 - Women in AI
2026-01-22 · 23:00
Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd. Date, Time and Location Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom! Align Before You Recommend The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements. While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning. By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items. Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage About the Speaker Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts. Generalizable Vision-Language Models: Challenges, Advances, and Future Directions Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings. About the Speaker Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM. Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved! About the Speaker Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist. FiftyOne Labs: Enabling experimentation for the computer vision community FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product. About the Speaker Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data. |
Jan 22 - Women in AI
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Jan 22 - Women in AI
2026-01-22 · 17:00
Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd. Date, Time and Location Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom! Align Before You Recommend The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements. While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning. By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items. Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage About the Speaker Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts. Generalizable Vision-Language Models: Challenges, Advances, and Future Directions Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings. About the Speaker Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM. Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved! About the Speaker Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist. FiftyOne Labs: Enabling experimentation for the computer vision community FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product. About the Speaker Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data. |
Jan 22 - Women in AI
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Jan 22 - Women in AI
2026-01-22 · 17:00
Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd. Date, Time and Location Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom! Align Before You Recommend The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements. While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning. By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items. Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage About the Speaker Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts. Generalizable Vision-Language Models: Challenges, Advances, and Future Directions Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings. About the Speaker Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM. Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved! About the Speaker Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist. FiftyOne Labs: Enabling experimentation for the computer vision community FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product. About the Speaker Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data. |
Jan 22 - Women in AI
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Jan 22 - Women in AI
2026-01-22 · 17:00
Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd. Date, Time and Location Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom! Align Before You Recommend The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements. While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning. By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items. Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage About the Speaker Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts. Generalizable Vision-Language Models: Challenges, Advances, and Future Directions Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings. About the Speaker Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM. Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved! About the Speaker Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist. FiftyOne Labs: Enabling experimentation for the computer vision community FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product. About the Speaker Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data. |
Jan 22 - Women in AI
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Data Science Retreat Demo Day #44
2026-01-10 · 16:30
Data Science Retreat presents 5 Machine Learning prototypes and projects by Batch 44 participants. Free to Attend! Hey Berlin Data Folks! We’re kicking off 2026 with our first data event of the year. Join us for project demos, fresh ideas, great conversations, and of course—pizza & drinks on us 🍕🍻 Don’t miss it! Agenda: 17:30 - Drinks and Networking 18:00 - Welcome & Introduction Followed by Project Presentations Project Ideas: 1. AI/ML - EV Battery Shape Classification Project by Uma Maheswari Obbani The project aims to automate the detection and classification of used electric vehicle (EV) batteries by shape — cylindrical, prismatic, or pouch — using AI/ML and computer vision techniques. The goal is to provide manufacturers and recyclers with real-time insights about battery type distribution, enabling efficient sorting, inventory management, and safety compliance. 2. Developing Scalable and Reliable Coffee Yield Forecasting Tools Project by Dr. Juan Fernando Duenas Serrano The aim of the project is short term coffee yield forecasting improves planning and value creation across the supply chain, from farmers to roasters. Current yield estimation methods are not scalable and require specialized expertise. This project explores computer vision as a scalable alternative using smartphone images. A YOLOv8 object detection model counts unripe Arabica coffee cherries on branches. The model can estimate yield at tree or farm level with optional user input. A Gradio or Streamlit web app will demonstrate, evaluate, and extend the model’s potential. 3. Classic Car Diagnostic Tool Project by Lulezim Ukaj This project aims to develop a specialized AI-powered diagnostic tool for classic cars that leverages community knowledge from German automotive forums. Unlike modern OBD-II–based diagnostic tools, which dominate the market, this solution targets pre-1996 vehicles where standardized electronic diagnostics do not exist. 4. Enhancing on-site audience audio Project by Thede Witschel This project develops an enterprise-grade AI platform that automates the extraction of ESG data, regulatory compliance checks, and peer benchmarking for companies. Utilizing NLP and machine learning, the system converts unstructured sustainability reports into standardized metrics, facilitating real-time compliance monitoring and competitive intelligence across various industries. Business Impact: Targets the rapidly growing ESG software market, serving investment firms, consulting companies, and institutional investors requiring automated analysis for portfolio decisions and regulatory compliance. 5. Ludus Automated Game Balancing: An AI-Driven Evolutionary Pipeline for Card Game Meta-Stability Project by Rami Aldrea This project addresses the critical and often labor-intensive challenge of balancing a complex digital card game meta, specifically focusing on a 70-card set. The primary objective was to develop and validate an automated, iterative pipeline capable of achieving and maintaining a stable, competitive game environment where no single card or archetype consistently dominates. The desired outcome was a game meta with a mean card win rate of 50%, minimal variance (ideally <5% standard deviation for competitive e-sports), and preserved strategic diversity, all while significantly reducing manual design time. 20:00 - Open for networking 20:30 - Wrap up We have limited seat so please RSVP soon. See you all at the event. |
Data Science Retreat Demo Day #44
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Discover AWS's cutting-edge NVIDIA GPU-powered instances, including the P6 family of instances powered by NVIDIA Blackwell GPUs. Explore how these computing solutions enable training and deployment of trillion-parameter models, support extra-large context windows, and deliver real-time performance for high-concurrency applications. Learn about AWS's innovations in instance security, reliability at scale, and infrastructure efficiency. Hear directly from customers about their experience running GPU-intensive workloads on AWS. Join us to understand how AWS's accelerated computing solutions can drive your AI initiatives forward. Learn more: More AWS events: https://go.aws/3kss9CP Subscribe: More AWS videos: http://bit.ly/2O3zS75 More AWS events videos: http://bit.ly/316g9t4 ABOUT AWS: Amazon Web Services (AWS) hosts events, both online and in-person, bringing the cloud computing community together to connect, collaborate, and learn from AWS experts. AWS is the world's most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster. AWSreInvent #AWSreInvent2025 #AWS |
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Training large AI models requires significant compute resources and can be time and cost intensive. In this session, learn to optimize and accelerate your model training workloads using AWS's purpose-built infrastructure and tools. We'll dive deep into leveraging services like Amazon SageMaker HyperPod for distributed training at scale and SageMaker fully managed training jobs for cost-effective ML acceleration. You'll learn to scale training across clusters using techniques like data and model parallelism, automated model tuning, and efficient checkpoint management. Through real customer examples, see how to reduce training time by up to 40%, optimize costs, and build high-performance training pipelines. Learn more: More AWS events: https://go.aws/3kss9CP Subscribe: More AWS videos: http://bit.ly/2O3zS75 More AWS events videos: http://bit.ly/316g9t4 ABOUT AWS: Amazon Web Services (AWS) hosts events, both online and in-person, bringing the cloud computing community together to connect, collaborate, and learn from AWS experts. AWS is the world's most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster. AWSreInvent #AWSreInvent2025 #AWS |
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AWS re:Invent 2025 - Streamline AI model development lifecycle with Amazon SageMaker AI (AIM364)
2025-12-03 · 07:04
Learn how Amazon SageMaker AI transforms model development with a unified environment for all your AI workloads - from interactive model development on IDEs to maximizing task and compute resource utilization across training and inference workloads. We'll demonstrate how to use SageMaker Studio as the familiar IDE to develop, submit, and monitor ML jobs, while leveraging the scalability and resiliency of HyperPod environment for computationally intensive tasks like training and deploying large foundation models. You will also learn how HyperPod task governance capability can help you automatically allocate compute resources based on task prioritization end-to-end through the model development lifecycle. Learn more: More AWS events: https://go.aws/3kss9CP Subscribe: More AWS videos: http://bit.ly/2O3zS75 More AWS events videos: http://bit.ly/316g9t4 ABOUT AWS: Amazon Web Services (AWS) hosts events, both online and in-person, bringing the cloud computing community together to connect, collaborate, and learn from AWS experts. AWS is the world's most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster. AWSreInvent #AWSreInvent2025 #AWS |
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Revolutionize file storage with PowerScale's Azure integration
2025-11-17 · 22:30
Stephen O'Leary
@ Dell
See how Dell PowerScale for Azure cuts through storage complexity and delivers enterprise-grade file storage built for modern workloads. Watch PowerScale handle sprawling, unstructured data and integrate effortlessly with Azure, fueling high-impact AI, analytics, and business-critical data-intensive applications without missing a beat. Join our 10-minute live session for a firsthand look at the high performance, rock-solid security, and operational speed customers demand today. |
Microsoft Ignite 2025 |
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Oct 16 - Visual AI in Agriculture (Day 2)
2025-10-16 · 16:00
Join us for day two 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 16 at 9 AM Pacific Location Virtual. Register for the Zoom. Field-Ready Vision: Building the Agricultural Image Repository (AgIR) for Sustainable Farming Data—not models—is the bottleneck in agricultural computer vision. This talk shares how Precision Sustainable Agriculture (PSA) is tackling that gap with the Agricultural Image Repository (AgIR): a cloud bank of high-resolution, labeled images spanning weeds (40+ species), cover crops, and cash crops across regions, seasons, and sensors. We’ll show how AgIR blends two complementary streams: (1) semi-field, high-throughput data captured by BenchBot, our open-source, modular gantry that autonomously images plants and feeds a semi-automated annotation pipeline; (2) true field images that capture real environmental variability. Together, they cut labeling cost, accelerate pretraining, and improve robustness in production. On top of AgIR, we’ve built a data-centric training stack: hierarchical augmentation groups, batch mixers, a stand-alone visualizer for rapid iteration, and a reproducible PyTorch Lightning pipeline. We’ll cover practical lessons from segmentation (crop/weed/residue/water/soil), handling domain shift between semi-field and field scenes, and designing metadata schemas that actually pay off at model time. About the Speaker Sina Baghbanijam is a Ph.D. candidate in Electrical and Computer Engineering at North Carolina State University, where his research centers on generative AI, computer vision, and machine learning. His work bridges advanced AI methods with real-world applications across agriculture, medicine, and the social sciences, with a focus on large-scale image segmentation, bias-aware modeling, and data-driven analysis. In addition to his academic research, Sina is currently serving as an Agricultural Image Repository Software Engineering Intern with Precision Sustainable Agriculture, where he develops scalable pipelines and metadata systems to support AI-driven analysis of crop, soil, and field imagery. Beyond Manual Measurements: How AI is Accelerating Plant Breeding Traditional plant breeding relies on manual phenotypic measurements that are time-intensive, subjective, and create bottlenecks in variety development. This presentation demonstrates how computer vision and artificial intelligence are revolutionizing plant selection processes by automating trait extraction from simple photographs. Our cloud-based platform transforms images captured with smartphones, drones, or laboratory cameras into instant, quantitative phenotypic data including fruit count, size measurements, and weight estimations. The system integrates phenotypic data with genotypic, pedigree, and environmental information in a unified database, enabling real-time analytics and decision support through intuitive dashboards. Unlike expensive hardware-dependent solutions, our software-focused approach works with existing camera equipment and standard breeding workflows, making advanced phenotyping accessible to organizations of all sizes. About the Speaker Dr. Sharon Inch is a botanist with a PhD in Plant Pathology and over 20 years of experience in horticulture and agricultural research. Throughout her career, she has witnessed firsthand the inefficiencies of traditional breeding methods, inspiring her to found AgriVision Analytics. As CEO, she leads the development of cloud-based computer vision platforms that transform plant breeding workflows through AI-powered phenotyping. Her work focuses on accelerating variety development and improving breeding decision-making through automated trait extraction and data integration. Dr. Sharon Inch is passionate about bridging the gap between advanced technology and practical agricultural applications to address global food security challenges. AI-assisted sweetpotato yield estimation pipelines using optical sensor data In this presentation, we will introduce the sensor systems and AI-powered analysis algorithms used in high-throughput sweetpotato post-harvest packing pipelines (developed by the Optical Sensing Lab at NC State University). By collecting image data from sweetpotato fields and packing lines respectively, we aim to quantitatively optimize the grading and yield estimation process, and the planning on storage and inventory-order matching. We built two customized sensor devices to collect data respectively from the top bins when receiving sweetpotatoes from farmers, and eliminator table before grading and packing process. We also developed a compact instance segmentation pipeline that can run on smart phones for rapid yield estimation in-field with resource limitations. To minimize data privacy concerns and Internet connectivity issues, we try to keep all the analysis pipelines on the edge, which results in a design tradeoff between resource availability and environmental constraints. We will also introduce sensor building with these considerations. The analysis results and real time production information are then integrated into an interactive online dashboard, where stakeholders can leverage to help with inventory-order management and making operational decisions. About the Speaker Yifan Wu is a current Ph.D candidate at NC State University working in the Optical Sensing Lab (OSL) supervised by Dr. Michael Kudenov. Research focuses on developing sensor systems and machine learning platforms for business intelligence applications. An End-to-End AgTech Use Case in FiftyOne The agricultural sector is increasingly turning to computer vision to tackle challenges in crop monitoring, pest detection, and yield optimization. Yet, developing robust models in this space often requires careful data exploration, curation, and evaluation—steps that are just as critical as model training itself. In this talk, we will walk through an end-to-end AgTech use case using FiftyOne, an open-source tool for dataset visualization, curation, and model evaluation. Starting with a pest detection dataset, we will explore the samples and annotations to understand dataset quality and potential pitfalls. From there, we will curate the dataset by filtering, tagging, and identifying edge cases that could impact downstream performance. Next, we’ll train a computer vision model to detect different pest species and demonstrate how FiftyOne can be used to rigorously evaluate the results. Along the way, we’ll highlight how dataset-centric workflows can accelerate experimentation, improve model reliability, and surface actionable insights specific to agricultural applications. By the end of the session, attendees will gain a practical understanding of how to: - Explore and diagnose real-world agricultural datasets - Curate training data for improved performance - Train and evaluate pest detection models - Use FiftyOne to close the loop between data and models This talk will be valuable for anyone working at the intersection of agriculture and computer vision, whether you’re building production models or just beginning to explore AgTech use cases. About the Speaker Prerna Dhareshwar is a Machine Learning Engineer at Voxel51, where she helps customers leverage FiftyOne to accelerate dataset curation, model development, and evaluation in real-world AI workflows. She brings extensive experience building and deploying computer vision and machine learning systems across industries. Prior to Voxel51, Prerna was a Senior Machine Learning Engineer at Instrumental Inc., where she developed models for defect detection in manufacturing, and a Machine Learning Software Engineer at Pure Storage, focusing on predictive analytics and automation. |
Oct 16 - Visual AI in Agriculture (Day 2)
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Oct 16 - Visual AI in Agriculture (Day 2)
2025-10-16 · 16:00
Join us for day two 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 16 at 9 AM Pacific Location Virtual. Register for the Zoom. Field-Ready Vision: Building the Agricultural Image Repository (AgIR) for Sustainable Farming Data—not models—is the bottleneck in agricultural computer vision. This talk shares how Precision Sustainable Agriculture (PSA) is tackling that gap with the Agricultural Image Repository (AgIR): a cloud bank of high-resolution, labeled images spanning weeds (40+ species), cover crops, and cash crops across regions, seasons, and sensors. We’ll show how AgIR blends two complementary streams: (1) semi-field, high-throughput data captured by BenchBot, our open-source, modular gantry that autonomously images plants and feeds a semi-automated annotation pipeline; (2) true field images that capture real environmental variability. Together, they cut labeling cost, accelerate pretraining, and improve robustness in production. On top of AgIR, we’ve built a data-centric training stack: hierarchical augmentation groups, batch mixers, a stand-alone visualizer for rapid iteration, and a reproducible PyTorch Lightning pipeline. We’ll cover practical lessons from segmentation (crop/weed/residue/water/soil), handling domain shift between semi-field and field scenes, and designing metadata schemas that actually pay off at model time. About the Speaker Sina Baghbanijam is a Ph.D. candidate in Electrical and Computer Engineering at North Carolina State University, where his research centers on generative AI, computer vision, and machine learning. His work bridges advanced AI methods with real-world applications across agriculture, medicine, and the social sciences, with a focus on large-scale image segmentation, bias-aware modeling, and data-driven analysis. In addition to his academic research, Sina is currently serving as an Agricultural Image Repository Software Engineering Intern with Precision Sustainable Agriculture, where he develops scalable pipelines and metadata systems to support AI-driven analysis of crop, soil, and field imagery. Beyond Manual Measurements: How AI is Accelerating Plant Breeding Traditional plant breeding relies on manual phenotypic measurements that are time-intensive, subjective, and create bottlenecks in variety development. This presentation demonstrates how computer vision and artificial intelligence are revolutionizing plant selection processes by automating trait extraction from simple photographs. Our cloud-based platform transforms images captured with smartphones, drones, or laboratory cameras into instant, quantitative phenotypic data including fruit count, size measurements, and weight estimations. The system integrates phenotypic data with genotypic, pedigree, and environmental information in a unified database, enabling real-time analytics and decision support through intuitive dashboards. Unlike expensive hardware-dependent solutions, our software-focused approach works with existing camera equipment and standard breeding workflows, making advanced phenotyping accessible to organizations of all sizes. About the Speaker Dr. Sharon Inch is a botanist with a PhD in Plant Pathology and over 20 years of experience in horticulture and agricultural research. Throughout her career, she has witnessed firsthand the inefficiencies of traditional breeding methods, inspiring her to found AgriVision Analytics. As CEO, she leads the development of cloud-based computer vision platforms that transform plant breeding workflows through AI-powered phenotyping. Her work focuses on accelerating variety development and improving breeding decision-making through automated trait extraction and data integration. Dr. Sharon Inch is passionate about bridging the gap between advanced technology and practical agricultural applications to address global food security challenges. AI-assisted sweetpotato yield estimation pipelines using optical sensor data In this presentation, we will introduce the sensor systems and AI-powered analysis algorithms used in high-throughput sweetpotato post-harvest packing pipelines (developed by the Optical Sensing Lab at NC State University). By collecting image data from sweetpotato fields and packing lines respectively, we aim to quantitatively optimize the grading and yield estimation process, and the planning on storage and inventory-order matching. We built two customized sensor devices to collect data respectively from the top bins when receiving sweetpotatoes from farmers, and eliminator table before grading and packing process. We also developed a compact instance segmentation pipeline that can run on smart phones for rapid yield estimation in-field with resource limitations. To minimize data privacy concerns and Internet connectivity issues, we try to keep all the analysis pipelines on the edge, which results in a design tradeoff between resource availability and environmental constraints. We will also introduce sensor building with these considerations. The analysis results and real time production information are then integrated into an interactive online dashboard, where stakeholders can leverage to help with inventory-order management and making operational decisions. About the Speaker Yifan Wu is a current Ph.D candidate at NC State University working in the Optical Sensing Lab (OSL) supervised by Dr. Michael Kudenov. Research focuses on developing sensor systems and machine learning platforms for business intelligence applications. An End-to-End AgTech Use Case in FiftyOne The agricultural sector is increasingly turning to computer vision to tackle challenges in crop monitoring, pest detection, and yield optimization. Yet, developing robust models in this space often requires careful data exploration, curation, and evaluation—steps that are just as critical as model training itself. In this talk, we will walk through an end-to-end AgTech use case using FiftyOne, an open-source tool for dataset visualization, curation, and model evaluation. Starting with a pest detection dataset, we will explore the samples and annotations to understand dataset quality and potential pitfalls. From there, we will curate the dataset by filtering, tagging, and identifying edge cases that could impact downstream performance. Next, we’ll train a computer vision model to detect different pest species and demonstrate how FiftyOne can be used to rigorously evaluate the results. Along the way, we’ll highlight how dataset-centric workflows can accelerate experimentation, improve model reliability, and surface actionable insights specific to agricultural applications. By the end of the session, attendees will gain a practical understanding of how to: - Explore and diagnose real-world agricultural datasets - Curate training data for improved performance - Train and evaluate pest detection models - Use FiftyOne to close the loop between data and models This talk will be valuable for anyone working at the intersection of agriculture and computer vision, whether you’re building production models or just beginning to explore AgTech use cases. About the Speaker Prerna Dhareshwar is a Machine Learning Engineer at Voxel51, where she helps customers leverage FiftyOne to accelerate dataset curation, model development, and evaluation in real-world AI workflows. She brings extensive experience building and deploying computer vision and machine learning systems across industries. Prior to Voxel51, Prerna was a Senior Machine Learning Engineer at Instrumental Inc., where she developed models for defect detection in manufacturing, and a Machine Learning Software Engineer at Pure Storage, focusing on predictive analytics and automation. |
Oct 16 - Visual AI in Agriculture (Day 2)
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Oct 16 - Visual AI in Agriculture (Day 2)
2025-10-16 · 16:00
Join us for day two 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 16 at 9 AM Pacific Location Virtual. Register for the Zoom. Field-Ready Vision: Building the Agricultural Image Repository (AgIR) for Sustainable Farming Data—not models—is the bottleneck in agricultural computer vision. This talk shares how Precision Sustainable Agriculture (PSA) is tackling that gap with the Agricultural Image Repository (AgIR): a cloud bank of high-resolution, labeled images spanning weeds (40+ species), cover crops, and cash crops across regions, seasons, and sensors. We’ll show how AgIR blends two complementary streams: (1) semi-field, high-throughput data captured by BenchBot, our open-source, modular gantry that autonomously images plants and feeds a semi-automated annotation pipeline; (2) true field images that capture real environmental variability. Together, they cut labeling cost, accelerate pretraining, and improve robustness in production. On top of AgIR, we’ve built a data-centric training stack: hierarchical augmentation groups, batch mixers, a stand-alone visualizer for rapid iteration, and a reproducible PyTorch Lightning pipeline. We’ll cover practical lessons from segmentation (crop/weed/residue/water/soil), handling domain shift between semi-field and field scenes, and designing metadata schemas that actually pay off at model time. About the Speaker Sina Baghbanijam is a Ph.D. candidate in Electrical and Computer Engineering at North Carolina State University, where his research centers on generative AI, computer vision, and machine learning. His work bridges advanced AI methods with real-world applications across agriculture, medicine, and the social sciences, with a focus on large-scale image segmentation, bias-aware modeling, and data-driven analysis. In addition to his academic research, Sina is currently serving as an Agricultural Image Repository Software Engineering Intern with Precision Sustainable Agriculture, where he develops scalable pipelines and metadata systems to support AI-driven analysis of crop, soil, and field imagery. Beyond Manual Measurements: How AI is Accelerating Plant Breeding Traditional plant breeding relies on manual phenotypic measurements that are time-intensive, subjective, and create bottlenecks in variety development. This presentation demonstrates how computer vision and artificial intelligence are revolutionizing plant selection processes by automating trait extraction from simple photographs. Our cloud-based platform transforms images captured with smartphones, drones, or laboratory cameras into instant, quantitative phenotypic data including fruit count, size measurements, and weight estimations. The system integrates phenotypic data with genotypic, pedigree, and environmental information in a unified database, enabling real-time analytics and decision support through intuitive dashboards. Unlike expensive hardware-dependent solutions, our software-focused approach works with existing camera equipment and standard breeding workflows, making advanced phenotyping accessible to organizations of all sizes. About the Speaker Dr. Sharon Inch is a botanist with a PhD in Plant Pathology and over 20 years of experience in horticulture and agricultural research. Throughout her career, she has witnessed firsthand the inefficiencies of traditional breeding methods, inspiring her to found AgriVision Analytics. As CEO, she leads the development of cloud-based computer vision platforms that transform plant breeding workflows through AI-powered phenotyping. Her work focuses on accelerating variety development and improving breeding decision-making through automated trait extraction and data integration. Dr. Sharon Inch is passionate about bridging the gap between advanced technology and practical agricultural applications to address global food security challenges. AI-assisted sweetpotato yield estimation pipelines using optical sensor data In this presentation, we will introduce the sensor systems and AI-powered analysis algorithms used in high-throughput sweetpotato post-harvest packing pipelines (developed by the Optical Sensing Lab at NC State University). By collecting image data from sweetpotato fields and packing lines respectively, we aim to quantitatively optimize the grading and yield estimation process, and the planning on storage and inventory-order matching. We built two customized sensor devices to collect data respectively from the top bins when receiving sweetpotatoes from farmers, and eliminator table before grading and packing process. We also developed a compact instance segmentation pipeline that can run on smart phones for rapid yield estimation in-field with resource limitations. To minimize data privacy concerns and Internet connectivity issues, we try to keep all the analysis pipelines on the edge, which results in a design tradeoff between resource availability and environmental constraints. We will also introduce sensor building with these considerations. The analysis results and real time production information are then integrated into an interactive online dashboard, where stakeholders can leverage to help with inventory-order management and making operational decisions. About the Speaker Yifan Wu is a current Ph.D candidate at NC State University working in the Optical Sensing Lab (OSL) supervised by Dr. Michael Kudenov. Research focuses on developing sensor systems and machine learning platforms for business intelligence applications. An End-to-End AgTech Use Case in FiftyOne The agricultural sector is increasingly turning to computer vision to tackle challenges in crop monitoring, pest detection, and yield optimization. Yet, developing robust models in this space often requires careful data exploration, curation, and evaluation—steps that are just as critical as model training itself. In this talk, we will walk through an end-to-end AgTech use case using FiftyOne, an open-source tool for dataset visualization, curation, and model evaluation. Starting with a pest detection dataset, we will explore the samples and annotations to understand dataset quality and potential pitfalls. From there, we will curate the dataset by filtering, tagging, and identifying edge cases that could impact downstream performance. Next, we’ll train a computer vision model to detect different pest species and demonstrate how FiftyOne can be used to rigorously evaluate the results. Along the way, we’ll highlight how dataset-centric workflows can accelerate experimentation, improve model reliability, and surface actionable insights specific to agricultural applications. By the end of the session, attendees will gain a practical understanding of how to: - Explore and diagnose real-world agricultural datasets - Curate training data for improved performance - Train and evaluate pest detection models - Use FiftyOne to close the loop between data and models This talk will be valuable for anyone working at the intersection of agriculture and computer vision, whether you’re building production models or just beginning to explore AgTech use cases. About the Speaker Prerna Dhareshwar is a Machine Learning Engineer at Voxel51, where she helps customers leverage FiftyOne to accelerate dataset curation, model development, and evaluation in real-world AI workflows. She brings extensive experience building and deploying computer vision and machine learning systems across industries. Prior to Voxel51, Prerna was a Senior Machine Learning Engineer at Instrumental Inc., where she developed models for defect detection in manufacturing, and a Machine Learning Software Engineer at Pure Storage, focusing on predictive analytics and automation. |
Oct 16 - Visual AI in Agriculture (Day 2)
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Oct 16 - Visual AI in Agriculture (Day 2)
2025-10-16 · 16:00
Join us for day two 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 16 at 9 AM Pacific Location Virtual. Register for the Zoom. Field-Ready Vision: Building the Agricultural Image Repository (AgIR) for Sustainable Farming Data—not models—is the bottleneck in agricultural computer vision. This talk shares how Precision Sustainable Agriculture (PSA) is tackling that gap with the Agricultural Image Repository (AgIR): a cloud bank of high-resolution, labeled images spanning weeds (40+ species), cover crops, and cash crops across regions, seasons, and sensors. We’ll show how AgIR blends two complementary streams: (1) semi-field, high-throughput data captured by BenchBot, our open-source, modular gantry that autonomously images plants and feeds a semi-automated annotation pipeline; (2) true field images that capture real environmental variability. Together, they cut labeling cost, accelerate pretraining, and improve robustness in production. On top of AgIR, we’ve built a data-centric training stack: hierarchical augmentation groups, batch mixers, a stand-alone visualizer for rapid iteration, and a reproducible PyTorch Lightning pipeline. We’ll cover practical lessons from segmentation (crop/weed/residue/water/soil), handling domain shift between semi-field and field scenes, and designing metadata schemas that actually pay off at model time. About the Speaker Sina Baghbanijam is a Ph.D. candidate in Electrical and Computer Engineering at North Carolina State University, where his research centers on generative AI, computer vision, and machine learning. His work bridges advanced AI methods with real-world applications across agriculture, medicine, and the social sciences, with a focus on large-scale image segmentation, bias-aware modeling, and data-driven analysis. In addition to his academic research, Sina is currently serving as an Agricultural Image Repository Software Engineering Intern with Precision Sustainable Agriculture, where he develops scalable pipelines and metadata systems to support AI-driven analysis of crop, soil, and field imagery. Beyond Manual Measurements: How AI is Accelerating Plant Breeding Traditional plant breeding relies on manual phenotypic measurements that are time-intensive, subjective, and create bottlenecks in variety development. This presentation demonstrates how computer vision and artificial intelligence are revolutionizing plant selection processes by automating trait extraction from simple photographs. Our cloud-based platform transforms images captured with smartphones, drones, or laboratory cameras into instant, quantitative phenotypic data including fruit count, size measurements, and weight estimations. The system integrates phenotypic data with genotypic, pedigree, and environmental information in a unified database, enabling real-time analytics and decision support through intuitive dashboards. Unlike expensive hardware-dependent solutions, our software-focused approach works with existing camera equipment and standard breeding workflows, making advanced phenotyping accessible to organizations of all sizes. About the Speaker Dr. Sharon Inch is a botanist with a PhD in Plant Pathology and over 20 years of experience in horticulture and agricultural research. Throughout her career, she has witnessed firsthand the inefficiencies of traditional breeding methods, inspiring her to found AgriVision Analytics. As CEO, she leads the development of cloud-based computer vision platforms that transform plant breeding workflows through AI-powered phenotyping. Her work focuses on accelerating variety development and improving breeding decision-making through automated trait extraction and data integration. Dr. Sharon Inch is passionate about bridging the gap between advanced technology and practical agricultural applications to address global food security challenges. AI-assisted sweetpotato yield estimation pipelines using optical sensor data In this presentation, we will introduce the sensor systems and AI-powered analysis algorithms used in high-throughput sweetpotato post-harvest packing pipelines (developed by the Optical Sensing Lab at NC State University). By collecting image data from sweetpotato fields and packing lines respectively, we aim to quantitatively optimize the grading and yield estimation process, and the planning on storage and inventory-order matching. We built two customized sensor devices to collect data respectively from the top bins when receiving sweetpotatoes from farmers, and eliminator table before grading and packing process. We also developed a compact instance segmentation pipeline that can run on smart phones for rapid yield estimation in-field with resource limitations. To minimize data privacy concerns and Internet connectivity issues, we try to keep all the analysis pipelines on the edge, which results in a design tradeoff between resource availability and environmental constraints. We will also introduce sensor building with these considerations. The analysis results and real time production information are then integrated into an interactive online dashboard, where stakeholders can leverage to help with inventory-order management and making operational decisions. About the Speaker Yifan Wu is a current Ph.D candidate at NC State University working in the Optical Sensing Lab (OSL) supervised by Dr. Michael Kudenov. Research focuses on developing sensor systems and machine learning platforms for business intelligence applications. An End-to-End AgTech Use Case in FiftyOne The agricultural sector is increasingly turning to computer vision to tackle challenges in crop monitoring, pest detection, and yield optimization. Yet, developing robust models in this space often requires careful data exploration, curation, and evaluation—steps that are just as critical as model training itself. In this talk, we will walk through an end-to-end AgTech use case using FiftyOne, an open-source tool for dataset visualization, curation, and model evaluation. Starting with a pest detection dataset, we will explore the samples and annotations to understand dataset quality and potential pitfalls. From there, we will curate the dataset by filtering, tagging, and identifying edge cases that could impact downstream performance. Next, we’ll train a computer vision model to detect different pest species and demonstrate how FiftyOne can be used to rigorously evaluate the results. Along the way, we’ll highlight how dataset-centric workflows can accelerate experimentation, improve model reliability, and surface actionable insights specific to agricultural applications. By the end of the session, attendees will gain a practical understanding of how to: - Explore and diagnose real-world agricultural datasets - Curate training data for improved performance - Train and evaluate pest detection models - Use FiftyOne to close the loop between data and models This talk will be valuable for anyone working at the intersection of agriculture and computer vision, whether you’re building production models or just beginning to explore AgTech use cases. About the Speaker Prerna Dhareshwar is a Machine Learning Engineer at Voxel51, where she helps customers leverage FiftyOne to accelerate dataset curation, model development, and evaluation in real-world AI workflows. She brings extensive experience building and deploying computer vision and machine learning systems across industries. Prior to Voxel51, Prerna was a Senior Machine Learning Engineer at Instrumental Inc., where she developed models for defect detection in manufacturing, and a Machine Learning Software Engineer at Pure Storage, focusing on predictive analytics and automation. |
Oct 16 - Visual AI in Agriculture (Day 2)
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Oct 16 - Visual AI in Agriculture (Day 2)
2025-10-16 · 16:00
Join us for day two 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 16 at 9 AM Pacific Location Virtual. Register for the Zoom. Field-Ready Vision: Building the Agricultural Image Repository (AgIR) for Sustainable Farming Data—not models—is the bottleneck in agricultural computer vision. This talk shares how Precision Sustainable Agriculture (PSA) is tackling that gap with the Agricultural Image Repository (AgIR): a cloud bank of high-resolution, labeled images spanning weeds (40+ species), cover crops, and cash crops across regions, seasons, and sensors. We’ll show how AgIR blends two complementary streams: (1) semi-field, high-throughput data captured by BenchBot, our open-source, modular gantry that autonomously images plants and feeds a semi-automated annotation pipeline; (2) true field images that capture real environmental variability. Together, they cut labeling cost, accelerate pretraining, and improve robustness in production. On top of AgIR, we’ve built a data-centric training stack: hierarchical augmentation groups, batch mixers, a stand-alone visualizer for rapid iteration, and a reproducible PyTorch Lightning pipeline. We’ll cover practical lessons from segmentation (crop/weed/residue/water/soil), handling domain shift between semi-field and field scenes, and designing metadata schemas that actually pay off at model time. About the Speaker Sina Baghbanijam is a Ph.D. candidate in Electrical and Computer Engineering at North Carolina State University, where his research centers on generative AI, computer vision, and machine learning. His work bridges advanced AI methods with real-world applications across agriculture, medicine, and the social sciences, with a focus on large-scale image segmentation, bias-aware modeling, and data-driven analysis. In addition to his academic research, Sina is currently serving as an Agricultural Image Repository Software Engineering Intern with Precision Sustainable Agriculture, where he develops scalable pipelines and metadata systems to support AI-driven analysis of crop, soil, and field imagery. Beyond Manual Measurements: How AI is Accelerating Plant Breeding Traditional plant breeding relies on manual phenotypic measurements that are time-intensive, subjective, and create bottlenecks in variety development. This presentation demonstrates how computer vision and artificial intelligence are revolutionizing plant selection processes by automating trait extraction from simple photographs. Our cloud-based platform transforms images captured with smartphones, drones, or laboratory cameras into instant, quantitative phenotypic data including fruit count, size measurements, and weight estimations. The system integrates phenotypic data with genotypic, pedigree, and environmental information in a unified database, enabling real-time analytics and decision support through intuitive dashboards. Unlike expensive hardware-dependent solutions, our software-focused approach works with existing camera equipment and standard breeding workflows, making advanced phenotyping accessible to organizations of all sizes. About the Speaker Dr. Sharon Inch is a botanist with a PhD in Plant Pathology and over 20 years of experience in horticulture and agricultural research. Throughout her career, she has witnessed firsthand the inefficiencies of traditional breeding methods, inspiring her to found AgriVision Analytics. As CEO, she leads the development of cloud-based computer vision platforms that transform plant breeding workflows through AI-powered phenotyping. Her work focuses on accelerating variety development and improving breeding decision-making through automated trait extraction and data integration. Dr. Sharon Inch is passionate about bridging the gap between advanced technology and practical agricultural applications to address global food security challenges. AI-assisted sweetpotato yield estimation pipelines using optical sensor data In this presentation, we will introduce the sensor systems and AI-powered analysis algorithms used in high-throughput sweetpotato post-harvest packing pipelines (developed by the Optical Sensing Lab at NC State University). By collecting image data from sweetpotato fields and packing lines respectively, we aim to quantitatively optimize the grading and yield estimation process, and the planning on storage and inventory-order matching. We built two customized sensor devices to collect data respectively from the top bins when receiving sweetpotatoes from farmers, and eliminator table before grading and packing process. We also developed a compact instance segmentation pipeline that can run on smart phones for rapid yield estimation in-field with resource limitations. To minimize data privacy concerns and Internet connectivity issues, we try to keep all the analysis pipelines on the edge, which results in a design tradeoff between resource availability and environmental constraints. We will also introduce sensor building with these considerations. The analysis results and real time production information are then integrated into an interactive online dashboard, where stakeholders can leverage to help with inventory-order management and making operational decisions. About the Speaker Yifan Wu is a current Ph.D candidate at NC State University working in the Optical Sensing Lab (OSL) supervised by Dr. Michael Kudenov. Research focuses on developing sensor systems and machine learning platforms for business intelligence applications. An End-to-End AgTech Use Case in FiftyOne The agricultural sector is increasingly turning to computer vision to tackle challenges in crop monitoring, pest detection, and yield optimization. Yet, developing robust models in this space often requires careful data exploration, curation, and evaluation—steps that are just as critical as model training itself. In this talk, we will walk through an end-to-end AgTech use case using FiftyOne, an open-source tool for dataset visualization, curation, and model evaluation. Starting with a pest detection dataset, we will explore the samples and annotations to understand dataset quality and potential pitfalls. From there, we will curate the dataset by filtering, tagging, and identifying edge cases that could impact downstream performance. Next, we’ll train a computer vision model to detect different pest species and demonstrate how FiftyOne can be used to rigorously evaluate the results. Along the way, we’ll highlight how dataset-centric workflows can accelerate experimentation, improve model reliability, and surface actionable insights specific to agricultural applications. By the end of the session, attendees will gain a practical understanding of how to: - Explore and diagnose real-world agricultural datasets - Curate training data for improved performance - Train and evaluate pest detection models - Use FiftyOne to close the loop between data and models This talk will be valuable for anyone working at the intersection of agriculture and computer vision, whether you’re building production models or just beginning to explore AgTech use cases. About the Speaker Prerna Dhareshwar is a Machine Learning Engineer at Voxel51, where she helps customers leverage FiftyOne to accelerate dataset curation, model development, and evaluation in real-world AI workflows. She brings extensive experience building and deploying computer vision and machine learning systems across industries. Prior to Voxel51, Prerna was a Senior Machine Learning Engineer at Instrumental Inc., where she developed models for defect detection in manufacturing, and a Machine Learning Software Engineer at Pure Storage, focusing on predictive analytics and automation. |
Oct 16 - Visual AI in Agriculture (Day 2)
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Oct 16 - Visual AI in Agriculture (Day 2)
2025-10-16 · 16:00
Join us for day two 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 16 at 9 AM Pacific Location Virtual. Register for the Zoom. Field-Ready Vision: Building the Agricultural Image Repository (AgIR) for Sustainable Farming Data—not models—is the bottleneck in agricultural computer vision. This talk shares how Precision Sustainable Agriculture (PSA) is tackling that gap with the Agricultural Image Repository (AgIR): a cloud bank of high-resolution, labeled images spanning weeds (40+ species), cover crops, and cash crops across regions, seasons, and sensors. We’ll show how AgIR blends two complementary streams: (1) semi-field, high-throughput data captured by BenchBot, our open-source, modular gantry that autonomously images plants and feeds a semi-automated annotation pipeline; (2) true field images that capture real environmental variability. Together, they cut labeling cost, accelerate pretraining, and improve robustness in production. On top of AgIR, we’ve built a data-centric training stack: hierarchical augmentation groups, batch mixers, a stand-alone visualizer for rapid iteration, and a reproducible PyTorch Lightning pipeline. We’ll cover practical lessons from segmentation (crop/weed/residue/water/soil), handling domain shift between semi-field and field scenes, and designing metadata schemas that actually pay off at model time. About the Speaker Sina Baghbanijam is a Ph.D. candidate in Electrical and Computer Engineering at North Carolina State University, where his research centers on generative AI, computer vision, and machine learning. His work bridges advanced AI methods with real-world applications across agriculture, medicine, and the social sciences, with a focus on large-scale image segmentation, bias-aware modeling, and data-driven analysis. In addition to his academic research, Sina is currently serving as an Agricultural Image Repository Software Engineering Intern with Precision Sustainable Agriculture, where he develops scalable pipelines and metadata systems to support AI-driven analysis of crop, soil, and field imagery. Beyond Manual Measurements: How AI is Accelerating Plant Breeding Traditional plant breeding relies on manual phenotypic measurements that are time-intensive, subjective, and create bottlenecks in variety development. This presentation demonstrates how computer vision and artificial intelligence are revolutionizing plant selection processes by automating trait extraction from simple photographs. Our cloud-based platform transforms images captured with smartphones, drones, or laboratory cameras into instant, quantitative phenotypic data including fruit count, size measurements, and weight estimations. The system integrates phenotypic data with genotypic, pedigree, and environmental information in a unified database, enabling real-time analytics and decision support through intuitive dashboards. Unlike expensive hardware-dependent solutions, our software-focused approach works with existing camera equipment and standard breeding workflows, making advanced phenotyping accessible to organizations of all sizes. About the Speaker Dr. Sharon Inch is a botanist with a PhD in Plant Pathology and over 20 years of experience in horticulture and agricultural research. Throughout her career, she has witnessed firsthand the inefficiencies of traditional breeding methods, inspiring her to found AgriVision Analytics. As CEO, she leads the development of cloud-based computer vision platforms that transform plant breeding workflows through AI-powered phenotyping. Her work focuses on accelerating variety development and improving breeding decision-making through automated trait extraction and data integration. Dr. Sharon Inch is passionate about bridging the gap between advanced technology and practical agricultural applications to address global food security challenges. AI-assisted sweetpotato yield estimation pipelines using optical sensor data In this presentation, we will introduce the sensor systems and AI-powered analysis algorithms used in high-throughput sweetpotato post-harvest packing pipelines (developed by the Optical Sensing Lab at NC State University). By collecting image data from sweetpotato fields and packing lines respectively, we aim to quantitatively optimize the grading and yield estimation process, and the planning on storage and inventory-order matching. We built two customized sensor devices to collect data respectively from the top bins when receiving sweetpotatoes from farmers, and eliminator table before grading and packing process. We also developed a compact instance segmentation pipeline that can run on smart phones for rapid yield estimation in-field with resource limitations. To minimize data privacy concerns and Internet connectivity issues, we try to keep all the analysis pipelines on the edge, which results in a design tradeoff between resource availability and environmental constraints. We will also introduce sensor building with these considerations. The analysis results and real time production information are then integrated into an interactive online dashboard, where stakeholders can leverage to help with inventory-order management and making operational decisions. About the Speaker Yifan Wu is a current Ph.D candidate at NC State University working in the Optical Sensing Lab (OSL) supervised by Dr. Michael Kudenov. Research focuses on developing sensor systems and machine learning platforms for business intelligence applications. An End-to-End AgTech Use Case in FiftyOne The agricultural sector is increasingly turning to computer vision to tackle challenges in crop monitoring, pest detection, and yield optimization. Yet, developing robust models in this space often requires careful data exploration, curation, and evaluation—steps that are just as critical as model training itself. In this talk, we will walk through an end-to-end AgTech use case using FiftyOne, an open-source tool for dataset visualization, curation, and model evaluation. Starting with a pest detection dataset, we will explore the samples and annotations to understand dataset quality and potential pitfalls. From there, we will curate the dataset by filtering, tagging, and identifying edge cases that could impact downstream performance. Next, we’ll train a computer vision model to detect different pest species and demonstrate how FiftyOne can be used to rigorously evaluate the results. Along the way, we’ll highlight how dataset-centric workflows can accelerate experimentation, improve model reliability, and surface actionable insights specific to agricultural applications. By the end of the session, attendees will gain a practical understanding of how to: - Explore and diagnose real-world agricultural datasets - Curate training data for improved performance - Train and evaluate pest detection models - Use FiftyOne to close the loop between data and models This talk will be valuable for anyone working at the intersection of agriculture and computer vision, whether you’re building production models or just beginning to explore AgTech use cases. About the Speaker Prerna Dhareshwar is a Machine Learning Engineer at Voxel51, where she helps customers leverage FiftyOne to accelerate dataset curation, model development, and evaluation in real-world AI workflows. She brings extensive experience building and deploying computer vision and machine learning systems across industries. Prior to Voxel51, Prerna was a Senior Machine Learning Engineer at Instrumental Inc., where she developed models for defect detection in manufacturing, and a Machine Learning Software Engineer at Pure Storage, focusing on predictive analytics and automation. |
Oct 16 - Visual AI in Agriculture (Day 2)
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Oct 16 - Visual AI in Agriculture (Day 2)
2025-10-16 · 16:00
Join us for day two 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 16 at 9 AM Pacific Location Virtual. Register for the Zoom. Field-Ready Vision: Building the Agricultural Image Repository (AgIR) for Sustainable Farming Data—not models—is the bottleneck in agricultural computer vision. This talk shares how Precision Sustainable Agriculture (PSA) is tackling that gap with the Agricultural Image Repository (AgIR): a cloud bank of high-resolution, labeled images spanning weeds (40+ species), cover crops, and cash crops across regions, seasons, and sensors. We’ll show how AgIR blends two complementary streams: (1) semi-field, high-throughput data captured by BenchBot, our open-source, modular gantry that autonomously images plants and feeds a semi-automated annotation pipeline; (2) true field images that capture real environmental variability. Together, they cut labeling cost, accelerate pretraining, and improve robustness in production. On top of AgIR, we’ve built a data-centric training stack: hierarchical augmentation groups, batch mixers, a stand-alone visualizer for rapid iteration, and a reproducible PyTorch Lightning pipeline. We’ll cover practical lessons from segmentation (crop/weed/residue/water/soil), handling domain shift between semi-field and field scenes, and designing metadata schemas that actually pay off at model time. About the Speaker Sina Baghbanijam is a Ph.D. candidate in Electrical and Computer Engineering at North Carolina State University, where his research centers on generative AI, computer vision, and machine learning. His work bridges advanced AI methods with real-world applications across agriculture, medicine, and the social sciences, with a focus on large-scale image segmentation, bias-aware modeling, and data-driven analysis. In addition to his academic research, Sina is currently serving as an Agricultural Image Repository Software Engineering Intern with Precision Sustainable Agriculture, where he develops scalable pipelines and metadata systems to support AI-driven analysis of crop, soil, and field imagery. Beyond Manual Measurements: How AI is Accelerating Plant Breeding Traditional plant breeding relies on manual phenotypic measurements that are time-intensive, subjective, and create bottlenecks in variety development. This presentation demonstrates how computer vision and artificial intelligence are revolutionizing plant selection processes by automating trait extraction from simple photographs. Our cloud-based platform transforms images captured with smartphones, drones, or laboratory cameras into instant, quantitative phenotypic data including fruit count, size measurements, and weight estimations. The system integrates phenotypic data with genotypic, pedigree, and environmental information in a unified database, enabling real-time analytics and decision support through intuitive dashboards. Unlike expensive hardware-dependent solutions, our software-focused approach works with existing camera equipment and standard breeding workflows, making advanced phenotyping accessible to organizations of all sizes. About the Speaker Dr. Sharon Inch is a botanist with a PhD in Plant Pathology and over 20 years of experience in horticulture and agricultural research. Throughout her career, she has witnessed firsthand the inefficiencies of traditional breeding methods, inspiring her to found AgriVision Analytics. As CEO, she leads the development of cloud-based computer vision platforms that transform plant breeding workflows through AI-powered phenotyping. Her work focuses on accelerating variety development and improving breeding decision-making through automated trait extraction and data integration. Dr. Sharon Inch is passionate about bridging the gap between advanced technology and practical agricultural applications to address global food security challenges. AI-assisted sweetpotato yield estimation pipelines using optical sensor data In this presentation, we will introduce the sensor systems and AI-powered analysis algorithms used in high-throughput sweetpotato post-harvest packing pipelines (developed by the Optical Sensing Lab at NC State University). By collecting image data from sweetpotato fields and packing lines respectively, we aim to quantitatively optimize the grading and yield estimation process, and the planning on storage and inventory-order matching. We built two customized sensor devices to collect data respectively from the top bins when receiving sweetpotatoes from farmers, and eliminator table before grading and packing process. We also developed a compact instance segmentation pipeline that can run on smart phones for rapid yield estimation in-field with resource limitations. To minimize data privacy concerns and Internet connectivity issues, we try to keep all the analysis pipelines on the edge, which results in a design tradeoff between resource availability and environmental constraints. We will also introduce sensor building with these considerations. The analysis results and real time production information are then integrated into an interactive online dashboard, where stakeholders can leverage to help with inventory-order management and making operational decisions. About the Speaker Yifan Wu is a current Ph.D candidate at NC State University working in the Optical Sensing Lab (OSL) supervised by Dr. Michael Kudenov. Research focuses on developing sensor systems and machine learning platforms for business intelligence applications. An End-to-End AgTech Use Case in FiftyOne The agricultural sector is increasingly turning to computer vision to tackle challenges in crop monitoring, pest detection, and yield optimization. Yet, developing robust models in this space often requires careful data exploration, curation, and evaluation—steps that are just as critical as model training itself. In this talk, we will walk through an end-to-end AgTech use case using FiftyOne, an open-source tool for dataset visualization, curation, and model evaluation. Starting with a pest detection dataset, we will explore the samples and annotations to understand dataset quality and potential pitfalls. From there, we will curate the dataset by filtering, tagging, and identifying edge cases that could impact downstream performance. Next, we’ll train a computer vision model to detect different pest species and demonstrate how FiftyOne can be used to rigorously evaluate the results. Along the way, we’ll highlight how dataset-centric workflows can accelerate experimentation, improve model reliability, and surface actionable insights specific to agricultural applications. By the end of the session, attendees will gain a practical understanding of how to: - Explore and diagnose real-world agricultural datasets - Curate training data for improved performance - Train and evaluate pest detection models - Use FiftyOne to close the loop between data and models This talk will be valuable for anyone working at the intersection of agriculture and computer vision, whether you’re building production models or just beginning to explore AgTech use cases. About the Speaker Prerna Dhareshwar is a Machine Learning Engineer at Voxel51, where she helps customers leverage FiftyOne to accelerate dataset curation, model development, and evaluation in real-world AI workflows. She brings extensive experience building and deploying computer vision and machine learning systems across industries. Prior to Voxel51, Prerna was a Senior Machine Learning Engineer at Instrumental Inc., where she developed models for defect detection in manufacturing, and a Machine Learning Software Engineer at Pure Storage, focusing on predictive analytics and automation. |
Oct 16 - Visual AI in Agriculture (Day 2)
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