Google SRE NYC proudly announces our last Google SRE NYC Tech Talk for 2025.

This event is co-sponsored by sentry.io. Thank you Sentry for your partnership!

Let's farewell 2025 with three amazing interactive short talks on Site Reliability and DevOps topics! As always the event will include an opportunity to mingle with the speakers and attendees over some light snacks and beverages after the talks.

The Meetup will take place on Tuesday, 16th of December 2025 at 6:00 PM at our Chelsea Markets office in NYC. The doors will open at 5:30 pm. Pls RSVP only if you're able to attend in-person, there will be no live streaming.

When RSVP'ing to this event, please enter your full name exactly as it appears on your government issued ID. You will be required to present your ID at check in.

Agenda: Paul Jaffre - Senior Developer Experience Engineer\, sentry.io One Trace to Rule Them All: Unifying Sentry Errors with OpenTelemetry tracing SREs face the challenge of operating reliable observability infrastructure while avoiding vendor lock-in from proprietary APM (Application Performance Monitoring) solutions. OpenTelemetry has become the standard for instrumenting applications, allowing teams to collect traces, metrics, and logs. But raw telemetry data isn't enough. SREs need tools to visualize, debug, and respond to production incidents quickly. Sentry now supports OTLP, enabling teams to send OpenTelemetry data directly to Sentry for analysis. This talk covers how Sentry's OTLP support works in practice: connecting frontend and backend traces across services, correlating logs with distributed traces, and using tools to identify slow queries and performance bottlenecks. We'll discuss the practical benefits for SREs, like faster incident resolution, better cross-team debugging, and the flexibility to change observability backends without re-instrumenting code. Paul’s background spans engineering, product management, UX design, and open source. He has a soft spot for dev tools and loses sleep over making things easy to understand and use. Paul has a dynamic professional background, from strategy to stability. His time at Krossover Intelligence established a strong foundation by blending Product Management with hands-on development, and he later focused on core reliability at MakerBot, where he implemented automated end-to-end testing and drove performance improvements. He then extended this expertise in stability and scale at Cypress.io, where he served as a Developer Experience Engineer, focusing on improving workflow, contribution, and usability for their widely adopted open-source community.

Thiara Ortiz - Cloud Gaming SRE Manager\, Netflix Managing Black Box Systems SREs often face ambiguity when managing black box systems (LLMs, Games, Poorly Understood Dependencies). We will discuss how Netflix monitors service health as black boxes using multiple measurement techniques to understand system behavior, aligning with the need for robust observability tools. These strategies are crucial for system reliability and user experience. By proactively identifying and resolving issues, we ensure smoother playback experience and maintain user trust, even as the platform continues to evolve and gain maturity. The principles shared within this talk can be expanded to other applications such as AI reliability in data quality and model deployments.

Thiara has worked at some of the largest internet companies in the world, Meta and Netflix. During her time at Meta, Thiara found a passion for distributed systems and bringing new hardware into production. Always curious to explore new solutions to complex problems, Thiara developed Fleet Scanner, internally known as Lemonaid, to perform memory, compute, and storage benchmarks on each Meta server in production. This service runs on over 5 million servers and continues to be utilized at Meta. Since Meta, Thiara has been working at Netflix as a Senior CDN Reliability engineer, and now, Cloud Gaming SRE Manager. When incidents occur and Netflix's systems do not behave as expected, Thiara can be found working and engaging the necessary teams to remediate these issues.

Andrew Espira - Platform and Site Reliability Engineer\, Founding Engineer kustode ML-Powered Predictive SRE: Using Behavioral Signals to Prevent Cluster Inefficiencies Before They Impact Production SREs managing ML clusters often discover resource inefficiencies and queue bottlenecks only after they've impacted production services. This talk presents a machine learning approach to predict these issues before they occur, transforming SRE from reactive firefighting to proactive system optimization. We demonstrate how to build predictive models using production cluster traces that identify two critical failure modes: (1) GPU under-utilization relative to requested resources, and (2) abnormal queue wait times that indicate impending service degradation. The SRE practitioners will learn how to extract early warning indicators from standard cluster logs, build ML models that provide actionable confidence scores for operational decisions, and take practical steps to integrate predictive analytics into existing SRE toolchains to achieve 50%+ reduction in resource waste and queue-related incidents This talk bridges the gap between traditional SRE observability and modern predictive analytics, showing how teams can evolve from reactive monitoring to intelligent, forward-looking reliability engineering" Andrew has over 8 years of experience architecting and maintaining large-scale distributed systems. He is the Founding Engineer of Kustode (kustode.com), where he develops cutting-edge reliability and observability solutions for modern infrastructure in the Insurance and health care solutions space. Currently pursuing graduate studies in Data Science at Saint Peter's University, he specializes in the intersection of reliability engineering and artificial intelligence. His research focuses on applying machine learning to operational challenges, with publications in peer-reviewed venues including ScienceDirect. He's passionate about making complex systems more predictable and maintainable through data-driven approaches. When not optimizing cluster performance or building the next generation of observability tools, Andrew enjoys contributing to open-source projects and mentoring early-career engineers in the SRE community.

Our Tech Talks series are for professional development and networking: no recruiters, sales or press please! Google is committed to providing a harassment-free and inclusive conference experience for everyone, and all participants must follow our Event Community Guidelines. The event will be photographed and video recorded.

Event space is limited! A reservation is required to attend. Reserve your spot today and share the event details with your SRE/DevOps friends 🙂

Explore the cutting edge of AI with Strands Agents—autonomous systems that evolve and learn continuously. We'll demonstrate advanced agents that can identify knowledge gaps, self-modify reasoning strategies, and dynamically build tools. These systems learn from interactions, improving decision-making without human intervention while communicating through multiple protocols and real-time, bi-directional streaming. Using Strands' model-driven approach, agents operate independently for extended periods, continuously enhancing effectiveness. Through real-world examples, see how self-improving agents have transformed business processes by adapting to changing requirements automatically. Join us to challenge conventional thinking about agent limitations and reshape your approach to building truly autonomous AI systems.

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

Please register here

Platform engineering teams often face a persistent challenge: fast, reliable access to high-quality test data. Production data is restricted, masking is slow, and manual dataset creation becomes a bottleneck across development, testing, and AI workflows. Agentic AI now makes it possible to automate this entire process and deliver synthetic data on demand in the exact formats your teams need.

Join synthetic data expert Mark Brocato as he demonstrates how Tonic Fabricate removes data friction by generating realistic, structured synthetic test data automatically. See how platform teams can use this capability to support developers and AI practitioners with quick, compliant, and self-service access to data, exported directly into supported databases or delivered as JSON, PDFs, DOCX, EML, and more.What you'll learn during the webinar:

• How to select the right approach to synthetic data: Compare rule-based synthesis, de-identification, and Agentic AI to determine which method best supports your platform and your teams.
• How to generate highly realistic, nuanced data: Learn how Agentic AI creates complex test data for development and testing, significantly reducing the need for deep domain expertise.
• Best practices and practical platform use cases: See how automated synthetic data fits into platform engineering responsibilities, including CI/CD pipelines, API mocking, developer self-service, test automation, and AI model development.

Speaker: Mark Brocato - Head of Engineering for Fabricate @ Tonic.ai

Mark Brocato is a software developer and entrepreneur best known as the founder of Mockaroo, one of the world’s leading synthetic data generators, launched in 2014. The idea for Mockaroo came while Mark was watching QA engineers struggle to test complex life science workflows at a startup called BioFortis, inspiring him to make realistic test data easier for everyone. With over two decades in software development, he’s built tools for developers at Sencha, Layer0, and beyond. In 2024, Mark launched Fabricate, the AI-powered synthetic data platform that was acquired by Tonic.ai in 2025, where Mark continues to lead its development. A Ruby, JavaScript, and Rust developer, he divides his time between Sparta, New Jersey, and Tallinn, Estonia.

Please register here

Platform engineering teams often face a persistent challenge: fast, reliable access to high-quality test data. Production data is restricted, masking is slow, and manual dataset creation becomes a bottleneck across development, testing, and AI workflows. Agentic AI now makes it possible to automate this entire process and deliver synthetic data on demand in the exact formats your teams need.

Join synthetic data expert Mark Brocato as he demonstrates how Tonic Fabricate removes data friction by generating realistic, structured synthetic test data automatically. See how platform teams can use this capability to support developers and AI practitioners with quick, compliant, and self-service access to data, exported directly into supported databases or delivered as JSON, PDFs, DOCX, EML, and more.What you'll learn during the webinar:

• How to select the right approach to synthetic data: Compare rule-based synthesis, de-identification, and Agentic AI to determine which method best supports your platform and your teams.
• How to generate highly realistic, nuanced data: Learn how Agentic AI creates complex test data for development and testing, significantly reducing the need for deep domain expertise.
• Best practices and practical platform use cases: See how automated synthetic data fits into platform engineering responsibilities, including CI/CD pipelines, API mocking, developer self-service, test automation, and AI model development.

Speaker: Mark Brocato - Head of Engineering for Fabricate @ Tonic.ai

Mark Brocato is a software developer and entrepreneur best known as the founder of Mockaroo, one of the world’s leading synthetic data generators, launched in 2014. The idea for Mockaroo came while Mark was watching QA engineers struggle to test complex life science workflows at a startup called BioFortis, inspiring him to make realistic test data easier for everyone. With over two decades in software development, he’s built tools for developers at Sencha, Layer0, and beyond. In 2024, Mark launched Fabricate, the AI-powered synthetic data platform that was acquired by Tonic.ai in 2025, where Mark continues to lead its development. A Ruby, JavaScript, and Rust developer, he divides his time between Sparta, New Jersey, and Tallinn, Estonia.

Please register here

Platform engineering teams often face a persistent challenge: fast, reliable access to high-quality test data. Production data is restricted, masking is slow, and manual dataset creation becomes a bottleneck across development, testing, and AI workflows. Agentic AI now makes it possible to automate this entire process and deliver synthetic data on demand in the exact formats your teams need.

Join synthetic data expert Mark Brocato as he demonstrates how Tonic Fabricate removes data friction by generating realistic, structured synthetic test data automatically. See how platform teams can use this capability to support developers and AI practitioners with quick, compliant, and self-service access to data, exported directly into supported databases or delivered as JSON, PDFs, DOCX, EML, and more.What you'll learn during the webinar:

• How to select the right approach to synthetic data: Compare rule-based synthesis, de-identification, and Agentic AI to determine which method best supports your platform and your teams.
• How to generate highly realistic, nuanced data: Learn how Agentic AI creates complex test data for development and testing, significantly reducing the need for deep domain expertise.
• Best practices and practical platform use cases: See how automated synthetic data fits into platform engineering responsibilities, including CI/CD pipelines, API mocking, developer self-service, test automation, and AI model development.

Speaker: Mark Brocato - Head of Engineering for Fabricate @ Tonic.ai

Mark Brocato is a software developer and entrepreneur best known as the founder of Mockaroo, one of the world’s leading synthetic data generators, launched in 2014. The idea for Mockaroo came while Mark was watching QA engineers struggle to test complex life science workflows at a startup called BioFortis, inspiring him to make realistic test data easier for everyone. With over two decades in software development, he’s built tools for developers at Sencha, Layer0, and beyond. In 2024, Mark launched Fabricate, the AI-powered synthetic data platform that was acquired by Tonic.ai in 2025, where Mark continues to lead its development. A Ruby, JavaScript, and Rust developer, he divides his time between Sparta, New Jersey, and Tallinn, Estonia.

Please register here

Platform engineering teams often face a persistent challenge: fast, reliable access to high-quality test data. Production data is restricted, masking is slow, and manual dataset creation becomes a bottleneck across development, testing, and AI workflows. Agentic AI now makes it possible to automate this entire process and deliver synthetic data on demand in the exact formats your teams need.

Join synthetic data expert Mark Brocato as he demonstrates how Tonic Fabricate removes data friction by generating realistic, structured synthetic test data automatically. See how platform teams can use this capability to support developers and AI practitioners with quick, compliant, and self-service access to data, exported directly into supported databases or delivered as JSON, PDFs, DOCX, EML, and more.What you'll learn during the webinar:

• How to select the right approach to synthetic data: Compare rule-based synthesis, de-identification, and Agentic AI to determine which method best supports your platform and your teams.
• How to generate highly realistic, nuanced data: Learn how Agentic AI creates complex test data for development and testing, significantly reducing the need for deep domain expertise.
• Best practices and practical platform use cases: See how automated synthetic data fits into platform engineering responsibilities, including CI/CD pipelines, API mocking, developer self-service, test automation, and AI model development.

Speaker: Mark Brocato - Head of Engineering for Fabricate @ Tonic.ai

Mark Brocato is a software developer and entrepreneur best known as the founder of Mockaroo, one of the world’s leading synthetic data generators, launched in 2014. The idea for Mockaroo came while Mark was watching QA engineers struggle to test complex life science workflows at a startup called BioFortis, inspiring him to make realistic test data easier for everyone. With over two decades in software development, he’s built tools for developers at Sencha, Layer0, and beyond. In 2024, Mark launched Fabricate, the AI-powered synthetic data platform that was acquired by Tonic.ai in 2025, where Mark continues to lead its development. A Ruby, JavaScript, and Rust developer, he divides his time between Sparta, New Jersey, and Tallinn, Estonia.

Please register here

Platform engineering teams often face a persistent challenge: fast, reliable access to high-quality test data. Production data is restricted, masking is slow, and manual dataset creation becomes a bottleneck across development, testing, and AI workflows. Agentic AI now makes it possible to automate this entire process and deliver synthetic data on demand in the exact formats your teams need.

Join synthetic data expert Mark Brocato as he demonstrates how Tonic Fabricate removes data friction by generating realistic, structured synthetic test data automatically. See how platform teams can use this capability to support developers and AI practitioners with quick, compliant, and self-service access to data, exported directly into supported databases or delivered as JSON, PDFs, DOCX, EML, and more.What you'll learn during the webinar:

• How to select the right approach to synthetic data: Compare rule-based synthesis, de-identification, and Agentic AI to determine which method best supports your platform and your teams.
• How to generate highly realistic, nuanced data: Learn how Agentic AI creates complex test data for development and testing, significantly reducing the need for deep domain expertise.
• Best practices and practical platform use cases: See how automated synthetic data fits into platform engineering responsibilities, including CI/CD pipelines, API mocking, developer self-service, test automation, and AI model development.

Speaker: Mark Brocato - Head of Engineering for Fabricate @ Tonic.ai

Mark Brocato is a software developer and entrepreneur best known as the founder of Mockaroo, one of the world’s leading synthetic data generators, launched in 2014. The idea for Mockaroo came while Mark was watching QA engineers struggle to test complex life science workflows at a startup called BioFortis, inspiring him to make realistic test data easier for everyone. With over two decades in software development, he’s built tools for developers at Sencha, Layer0, and beyond. In 2024, Mark launched Fabricate, the AI-powered synthetic data platform that was acquired by Tonic.ai in 2025, where Mark continues to lead its development. A Ruby, JavaScript, and Rust developer, he divides his time between Sparta, New Jersey, and Tallinn, Estonia.

Many enterprises want to build a culture of AI innovation and manage it at scale. Learn how a leading financial institution implemented a unified data and AI platform with built-in governance to solve this challenge. Using the next generation of Amazon SageMaker, their petabyte-scale solution enables users to discover and access data in a cloud-native marketplace then build in a unified data and AI development environment. This session is ideal for data and platform leaders looking to balance self-service capabilities with robust governance to enable AI initiatives.

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

❄️ DSF WinterFest 2025: Global Online Summit ❄️

Join the global data celebration!

Monday 24th to Friday 28th November 2025 Online \| 2-3 sessions per day \| Theme: Innovating with Data

DSF WinterFest is back, and this year, it’s going global! Join our 50,000-strong community for a week of world-class talks, tutorials, and panels exploring how data, AI, and analytics are reshaping the world. Expect inspiring content, expert insights, and the cosy, welcoming DSF atmosphere we are known for, all from the comfort of your own space!

Why join? 🌍 A global stage with speakers and attendees from every corner of the world 🎟️ One ticket for the full week. Register once and access every session 💻 Easy access from anywhere. Join live or catch replays in your own time ☕ Cosy community vibe. No travel, no stress, just data and connection

➡️ REGISTER HERE FOR FREE! ⬅️

🎟️ Tickets:

Choose your experience and secure your spot today: Free Pass - Watch live and enjoy replays until 30 November 2025 Upgrade at Checkout - Get extended replay access until May 2026

Register on our website to receive your joining links, add sessions to your calendar, and tune in live from anywhere in the world.

Please note: Clicking “Attend” on Meetup does not register you for this summit. You must register via our website to receive your links.

🎁 Competition:

We’re spreading festive cheer! One lucky attendee will win a £300 Amazon gift voucher (or equivalent in your currency). Find out more here.

❄️❄️❄️

Session details:

💡 Competitor Response Planning Suite - Turning Threats into Strategic Advantage 🗓️ Wednesday 26th November ⏰ 12:30 PM (GMT) 🗣️ Rajat Kumar, Applied Data Science Manager @ dunnhumby and Namish Kaushik, Senior Applied Data Scientist @ dunnhumby

In today’s hyper-competitive retail environment, the presence of a competitor store isn’t just a challenge - it’s a direct threat to customer loyalty, sales, and profitability. This session introduces the Competitor Response Planning (CRP) Suite, a pioneering solution that fuses geo-spatial intelligence with predictive modeling to help retailers proactively counter competitive impact.

Built on the theme of “Innovating with Data”, the CRP Suite is a three-pronged modeling framework designed to predict customer churn risk across New Competitor Advent, Existing Competitor Landscapes and Instore Media Optimization.

The session will showcase how the CRP Suite integrates retailer data with third-party sources to extract key variables—store DNA, customer behavior, and geo-location—and trains custom propensity models tailored to different competitor types. This enables targeted retention strategies that replace generic mass offers with personalized interventions.

Attendees will also explore how the CRP Suite has been productionized into the Voila dashboard, empowering non-technical users to self-serve insights, trigger models, and navigate competitive threats with ease. Whether you're a data scientist, marketer, or business strategist, this session will demonstrate how data innovation can transform competitive threats into strategic opportunities—driving measurable outcomes for retailers and delighting customers through personalization.

➡️ REGISTER HERE FOR FREE! ⬅️

❄️❄️❄️

🔗 How to join:

Once registered, you’ll receive your unique joining link by email, plus handy reminders one week, one day, and one hour before each session. Don't forget to add the sessions you are attending to your calendar. If you can’t make it live, don’t worry, your ticket includes replay access until 30 November 2025 (or May 2026 with the upgrade).

📘 Reminders:

Time zones: All sessions are listed in GMT - please check your local time when registering.

Recordings: Access replays until 30 November 2025 with a free pass, or until May 2026 with an upgraded ticket

Please note: Clicking “Attend” on Meetup does not register you for this summit. You must register via our website to receive your links.

➡️ REGISTER HERE FOR FREE! ⬅️

Join the Celebration ❄️ Five days. Global speakers. Cutting-edge insights. Free to join live - replays included. Upgrade for extended access. Register now and be part of the global data community shaping the future. #DSFWinterFest

Welcome to the Best of ICCV series, your virtual pass to some of the groundbreaking research, insights, and innovations that defined this year’s conference. Live streaming from the authors to you.

Date, Time and Location

Nov 21, 2025 9 AM Pacific Online. Register for the Zoom!

GECO: Geometrically Consistent Embedding with Lightspeed Inference

Recent advances in feature learning have shown that self-supervised vision foundation models can capture semantic correspondences but often lack awareness of underlying 3D geometry. GECO addresses this gap by producing geometrically coherent features that semantically distinguish parts based on geometry (e.g., left/right eyes, front/back legs). We propose a training framework based on optimal transport, enabling supervision beyond keypoints, even under occlusions and disocclusions. With a lightweight architecture, GECO runs at 30 fps, 98.2% faster than prior methods, while achieving state-of-the-art performance on PFPascal, APK, and CUB, improving PCK by 6.0%, 6.2%, and 4.1%, respectively. Finally, we show that PCK alone is insufficient to capture geometric quality and introduce new metrics and insights for more geometry-aware feature learning

About the Speaker

Regine Hartwig is a PHD Graduate Student at the Technical University of Munich

Proactive Comorbidity Prediction in HIV: Towards Fair and Trustworthy Care

HIV is a chronic infection that weakens the immune system and exposes patients to a high burden of comorbidities. While antiretroviral therapy has improved life expectancy, comorbidities remain a major challenge, and traditional screening protocols often fail to capture subtle risk patterns early enough. To address this, we develop a novel method trained on lab tests and demographic data from 2,200 patients in SE London. The method integrates feature interaction modeling, attention mechanisms, residual fusion and label-specific attention heads, outperforming TabNet, MLPs and classical machine learning models.

Our experiments show that incorporating demographic information improves predictive performance, though demographic recoverability analyses reveal that age and gender can still be inferred from lab data alone, raising fairness concerns. Finally, robustness checks confirm stable feature importance across cross-validation folds, reinforcing the trustworthiness of our approach.

About the Speaker

Dimitrios Kollias is an Associate Professor in Multimodal AI at Queen Mary University of London, specializing in machine/deep learning, trustworthy AI, computer vision, medical imaging & healthcare, behavior analysis, HMI. I have published 80+ papers (h-index 39; 6100+ citations) in top venues (e.g., CVPR, ICCV, ECCV, AAAI, IJCV, ECAI), invented a patent in behavior analysis (Huawei) and my research is widely adopted by academia and industry. I also serve as AI consultant and advisor to global companies, and have played leading roles in major international AI workshops and competitions.

Toward Trustworthy Embodied Agents: From Individuals to Teams

Modern intelligent embodied agents, such as service robots and autonomous vehicles, interact frequently with humans in dynamic, uncertain environments. They may also collaborate with each other as a team through effective communication to enhance task success, safety, and efficiency. These brings a few significant challenges. First, building reliable agents that safely navigate multi-agent scenarios requires scalable and generalizable prediction of surrounding agents’ behaviors and robust decision making under environmental uncertainty in out-of-distribution (OOD) scenarios. Second, effective cooperation between agents requires efficient communication and information fusion strategies and reliable task planning for complex long-horizon tasks.

In this talk, I will introduce a series of our recent work that addresses these challenges to enable safe and trustworthy embodied agents and their application to autonomous driving and service robots. Specifically, I will first demonstrate principled uncertainty quantification techniques and how they enable generalizable prediction and planning in out-of-distribution scenarios. Then, I will talk about effective approaches to enable efficient multi-agent communication and cooperation in centralized and decentralized settings.

About the Speaker

Dr. Jiachen Li is an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) and a cooperating faculty in the Department of Computer Science and Engineering (CSE) at the University of California, Riverside. He is the Director of the Trustworthy Autonomous Systems Laboratory and is affiliated with the Riverside Artificial Intelligence Research Institute (RAISE), the Center for Robotics and Intelligent Systems (CRIS), and the Center for Environmental Research and Technology (CE-CERT).

DRaM-LHM: A Quaternion Framework for Iterative Camera Pose Estimation

We explore a quaternion adjugate matrix-based representation for rotational motion in the Perspective-n-Point (PnP) problem. Leveraging quadratic quaternion terms within a Determinant Ratio Matrix (DRaM) estimation framework, we extend its application to perspective scenarios, providing a robust and efficient initialization for iterative PnP pose estimation. Notably, by solving the orthographic projection least-squares problem, DRaM provides a reliable initialization that enhances the accuracy and stability of iterative PnP solvers. Experiments on synthetic and real data demonstrate its efficiency, accuracy, and robustness, particularly under high noise conditions. Furthermore, our nonminimal formulation ensures numerical stability, making it effective for real-world applications.

About the Speaker

Chen Lin was a Research Fellow at the Simons Foundation, where she specialized in 3D computer vision and visual(-inertial) SLAM. Her research spans from classical multiview geometry to learning-based pose estimation and scene understanding. Her ICCV 2025 paper introduces a new framework for rotation and pose estimation built on advanced algebraic paradigms.

Welcome to the Best of ICCV series, your virtual pass to some of the groundbreaking research, insights, and innovations that defined this year’s conference. Live streaming from the authors to you.

Date, Time and Location

Nov 21, 2025 9 AM Pacific Online. Register for the Zoom!

GECO: Geometrically Consistent Embedding with Lightspeed Inference

Recent advances in feature learning have shown that self-supervised vision foundation models can capture semantic correspondences but often lack awareness of underlying 3D geometry. GECO addresses this gap by producing geometrically coherent features that semantically distinguish parts based on geometry (e.g., left/right eyes, front/back legs). We propose a training framework based on optimal transport, enabling supervision beyond keypoints, even under occlusions and disocclusions. With a lightweight architecture, GECO runs at 30 fps, 98.2% faster than prior methods, while achieving state-of-the-art performance on PFPascal, APK, and CUB, improving PCK by 6.0%, 6.2%, and 4.1%, respectively. Finally, we show that PCK alone is insufficient to capture geometric quality and introduce new metrics and insights for more geometry-aware feature learning

About the Speaker

Regine Hartwig is a PHD Graduate Student at the Technical University of Munich

Proactive Comorbidity Prediction in HIV: Towards Fair and Trustworthy Care

HIV is a chronic infection that weakens the immune system and exposes patients to a high burden of comorbidities. While antiretroviral therapy has improved life expectancy, comorbidities remain a major challenge, and traditional screening protocols often fail to capture subtle risk patterns early enough. To address this, we develop a novel method trained on lab tests and demographic data from 2,200 patients in SE London. The method integrates feature interaction modeling, attention mechanisms, residual fusion and label-specific attention heads, outperforming TabNet, MLPs and classical machine learning models.

Our experiments show that incorporating demographic information improves predictive performance, though demographic recoverability analyses reveal that age and gender can still be inferred from lab data alone, raising fairness concerns. Finally, robustness checks confirm stable feature importance across cross-validation folds, reinforcing the trustworthiness of our approach.

About the Speaker

Dimitrios Kollias is an Associate Professor in Multimodal AI at Queen Mary University of London, specializing in machine/deep learning, trustworthy AI, computer vision, medical imaging & healthcare, behavior analysis, HMI. I have published 80+ papers (h-index 39; 6100+ citations) in top venues (e.g., CVPR, ICCV, ECCV, AAAI, IJCV, ECAI), invented a patent in behavior analysis (Huawei) and my research is widely adopted by academia and industry. I also serve as AI consultant and advisor to global companies, and have played leading roles in major international AI workshops and competitions.

Toward Trustworthy Embodied Agents: From Individuals to Teams

Modern intelligent embodied agents, such as service robots and autonomous vehicles, interact frequently with humans in dynamic, uncertain environments. They may also collaborate with each other as a team through effective communication to enhance task success, safety, and efficiency. These brings a few significant challenges. First, building reliable agents that safely navigate multi-agent scenarios requires scalable and generalizable prediction of surrounding agents’ behaviors and robust decision making under environmental uncertainty in out-of-distribution (OOD) scenarios. Second, effective cooperation between agents requires efficient communication and information fusion strategies and reliable task planning for complex long-horizon tasks.

In this talk, I will introduce a series of our recent work that addresses these challenges to enable safe and trustworthy embodied agents and their application to autonomous driving and service robots. Specifically, I will first demonstrate principled uncertainty quantification techniques and how they enable generalizable prediction and planning in out-of-distribution scenarios. Then, I will talk about effective approaches to enable efficient multi-agent communication and cooperation in centralized and decentralized settings.

About the Speaker

Dr. Jiachen Li is an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) and a cooperating faculty in the Department of Computer Science and Engineering (CSE) at the University of California, Riverside. He is the Director of the Trustworthy Autonomous Systems Laboratory and is affiliated with the Riverside Artificial Intelligence Research Institute (RAISE), the Center for Robotics and Intelligent Systems (CRIS), and the Center for Environmental Research and Technology (CE-CERT).

DRaM-LHM: A Quaternion Framework for Iterative Camera Pose Estimation

We explore a quaternion adjugate matrix-based representation for rotational motion in the Perspective-n-Point (PnP) problem. Leveraging quadratic quaternion terms within a Determinant Ratio Matrix (DRaM) estimation framework, we extend its application to perspective scenarios, providing a robust and efficient initialization for iterative PnP pose estimation. Notably, by solving the orthographic projection least-squares problem, DRaM provides a reliable initialization that enhances the accuracy and stability of iterative PnP solvers. Experiments on synthetic and real data demonstrate its efficiency, accuracy, and robustness, particularly under high noise conditions. Furthermore, our nonminimal formulation ensures numerical stability, making it effective for real-world applications.

About the Speaker

Chen Lin was a Research Fellow at the Simons Foundation, where she specialized in 3D computer vision and visual(-inertial) SLAM. Her research spans from classical multiview geometry to learning-based pose estimation and scene understanding. Her ICCV 2025 paper introduces a new framework for rotation and pose estimation built on advanced algebraic paradigms.

Welcome to the Best of ICCV series, your virtual pass to some of the groundbreaking research, insights, and innovations that defined this year’s conference. Live streaming from the authors to you.

Date, Time and Location

Nov 21, 2025 9 AM Pacific Online. Register for the Zoom!

GECO: Geometrically Consistent Embedding with Lightspeed Inference

Recent advances in feature learning have shown that self-supervised vision foundation models can capture semantic correspondences but often lack awareness of underlying 3D geometry. GECO addresses this gap by producing geometrically coherent features that semantically distinguish parts based on geometry (e.g., left/right eyes, front/back legs). We propose a training framework based on optimal transport, enabling supervision beyond keypoints, even under occlusions and disocclusions. With a lightweight architecture, GECO runs at 30 fps, 98.2% faster than prior methods, while achieving state-of-the-art performance on PFPascal, APK, and CUB, improving PCK by 6.0%, 6.2%, and 4.1%, respectively. Finally, we show that PCK alone is insufficient to capture geometric quality and introduce new metrics and insights for more geometry-aware feature learning

About the Speaker

Regine Hartwig is a PHD Graduate Student at the Technical University of Munich

Proactive Comorbidity Prediction in HIV: Towards Fair and Trustworthy Care

HIV is a chronic infection that weakens the immune system and exposes patients to a high burden of comorbidities. While antiretroviral therapy has improved life expectancy, comorbidities remain a major challenge, and traditional screening protocols often fail to capture subtle risk patterns early enough. To address this, we develop a novel method trained on lab tests and demographic data from 2,200 patients in SE London. The method integrates feature interaction modeling, attention mechanisms, residual fusion and label-specific attention heads, outperforming TabNet, MLPs and classical machine learning models.

Our experiments show that incorporating demographic information improves predictive performance, though demographic recoverability analyses reveal that age and gender can still be inferred from lab data alone, raising fairness concerns. Finally, robustness checks confirm stable feature importance across cross-validation folds, reinforcing the trustworthiness of our approach.

About the Speaker

Dimitrios Kollias is an Associate Professor in Multimodal AI at Queen Mary University of London, specializing in machine/deep learning, trustworthy AI, computer vision, medical imaging & healthcare, behavior analysis, HMI. I have published 80+ papers (h-index 39; 6100+ citations) in top venues (e.g., CVPR, ICCV, ECCV, AAAI, IJCV, ECAI), invented a patent in behavior analysis (Huawei) and my research is widely adopted by academia and industry. I also serve as AI consultant and advisor to global companies, and have played leading roles in major international AI workshops and competitions.

Toward Trustworthy Embodied Agents: From Individuals to Teams

Modern intelligent embodied agents, such as service robots and autonomous vehicles, interact frequently with humans in dynamic, uncertain environments. They may also collaborate with each other as a team through effective communication to enhance task success, safety, and efficiency. These brings a few significant challenges. First, building reliable agents that safely navigate multi-agent scenarios requires scalable and generalizable prediction of surrounding agents’ behaviors and robust decision making under environmental uncertainty in out-of-distribution (OOD) scenarios. Second, effective cooperation between agents requires efficient communication and information fusion strategies and reliable task planning for complex long-horizon tasks.

In this talk, I will introduce a series of our recent work that addresses these challenges to enable safe and trustworthy embodied agents and their application to autonomous driving and service robots. Specifically, I will first demonstrate principled uncertainty quantification techniques and how they enable generalizable prediction and planning in out-of-distribution scenarios. Then, I will talk about effective approaches to enable efficient multi-agent communication and cooperation in centralized and decentralized settings.

About the Speaker

Dr. Jiachen Li is an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) and a cooperating faculty in the Department of Computer Science and Engineering (CSE) at the University of California, Riverside. He is the Director of the Trustworthy Autonomous Systems Laboratory and is affiliated with the Riverside Artificial Intelligence Research Institute (RAISE), the Center for Robotics and Intelligent Systems (CRIS), and the Center for Environmental Research and Technology (CE-CERT).

DRaM-LHM: A Quaternion Framework for Iterative Camera Pose Estimation

We explore a quaternion adjugate matrix-based representation for rotational motion in the Perspective-n-Point (PnP) problem. Leveraging quadratic quaternion terms within a Determinant Ratio Matrix (DRaM) estimation framework, we extend its application to perspective scenarios, providing a robust and efficient initialization for iterative PnP pose estimation. Notably, by solving the orthographic projection least-squares problem, DRaM provides a reliable initialization that enhances the accuracy and stability of iterative PnP solvers. Experiments on synthetic and real data demonstrate its efficiency, accuracy, and robustness, particularly under high noise conditions. Furthermore, our nonminimal formulation ensures numerical stability, making it effective for real-world applications.

About the Speaker

Chen Lin was a Research Fellow at the Simons Foundation, where she specialized in 3D computer vision and visual(-inertial) SLAM. Her research spans from classical multiview geometry to learning-based pose estimation and scene understanding. Her ICCV 2025 paper introduces a new framework for rotation and pose estimation built on advanced algebraic paradigms.

Welcome to the Best of ICCV series, your virtual pass to some of the groundbreaking research, insights, and innovations that defined this year’s conference. Live streaming from the authors to you.

Date, Time and Location

Nov 21, 2025 9 AM Pacific Online. Register for the Zoom!

GECO: Geometrically Consistent Embedding with Lightspeed Inference

Recent advances in feature learning have shown that self-supervised vision foundation models can capture semantic correspondences but often lack awareness of underlying 3D geometry. GECO addresses this gap by producing geometrically coherent features that semantically distinguish parts based on geometry (e.g., left/right eyes, front/back legs). We propose a training framework based on optimal transport, enabling supervision beyond keypoints, even under occlusions and disocclusions. With a lightweight architecture, GECO runs at 30 fps, 98.2% faster than prior methods, while achieving state-of-the-art performance on PFPascal, APK, and CUB, improving PCK by 6.0%, 6.2%, and 4.1%, respectively. Finally, we show that PCK alone is insufficient to capture geometric quality and introduce new metrics and insights for more geometry-aware feature learning

About the Speaker

Regine Hartwig is a PHD Graduate Student at the Technical University of Munich

Proactive Comorbidity Prediction in HIV: Towards Fair and Trustworthy Care

HIV is a chronic infection that weakens the immune system and exposes patients to a high burden of comorbidities. While antiretroviral therapy has improved life expectancy, comorbidities remain a major challenge, and traditional screening protocols often fail to capture subtle risk patterns early enough. To address this, we develop a novel method trained on lab tests and demographic data from 2,200 patients in SE London. The method integrates feature interaction modeling, attention mechanisms, residual fusion and label-specific attention heads, outperforming TabNet, MLPs and classical machine learning models.

Our experiments show that incorporating demographic information improves predictive performance, though demographic recoverability analyses reveal that age and gender can still be inferred from lab data alone, raising fairness concerns. Finally, robustness checks confirm stable feature importance across cross-validation folds, reinforcing the trustworthiness of our approach.

About the Speaker

Dimitrios Kollias is an Associate Professor in Multimodal AI at Queen Mary University of London, specializing in machine/deep learning, trustworthy AI, computer vision, medical imaging & healthcare, behavior analysis, HMI. I have published 80+ papers (h-index 39; 6100+ citations) in top venues (e.g., CVPR, ICCV, ECCV, AAAI, IJCV, ECAI), invented a patent in behavior analysis (Huawei) and my research is widely adopted by academia and industry. I also serve as AI consultant and advisor to global companies, and have played leading roles in major international AI workshops and competitions.

Toward Trustworthy Embodied Agents: From Individuals to Teams

Modern intelligent embodied agents, such as service robots and autonomous vehicles, interact frequently with humans in dynamic, uncertain environments. They may also collaborate with each other as a team through effective communication to enhance task success, safety, and efficiency. These brings a few significant challenges. First, building reliable agents that safely navigate multi-agent scenarios requires scalable and generalizable prediction of surrounding agents’ behaviors and robust decision making under environmental uncertainty in out-of-distribution (OOD) scenarios. Second, effective cooperation between agents requires efficient communication and information fusion strategies and reliable task planning for complex long-horizon tasks.

In this talk, I will introduce a series of our recent work that addresses these challenges to enable safe and trustworthy embodied agents and their application to autonomous driving and service robots. Specifically, I will first demonstrate principled uncertainty quantification techniques and how they enable generalizable prediction and planning in out-of-distribution scenarios. Then, I will talk about effective approaches to enable efficient multi-agent communication and cooperation in centralized and decentralized settings.

About the Speaker

Dr. Jiachen Li is an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) and a cooperating faculty in the Department of Computer Science and Engineering (CSE) at the University of California, Riverside. He is the Director of the Trustworthy Autonomous Systems Laboratory and is affiliated with the Riverside Artificial Intelligence Research Institute (RAISE), the Center for Robotics and Intelligent Systems (CRIS), and the Center for Environmental Research and Technology (CE-CERT).

DRaM-LHM: A Quaternion Framework for Iterative Camera Pose Estimation

We explore a quaternion adjugate matrix-based representation for rotational motion in the Perspective-n-Point (PnP) problem. Leveraging quadratic quaternion terms within a Determinant Ratio Matrix (DRaM) estimation framework, we extend its application to perspective scenarios, providing a robust and efficient initialization for iterative PnP pose estimation. Notably, by solving the orthographic projection least-squares problem, DRaM provides a reliable initialization that enhances the accuracy and stability of iterative PnP solvers. Experiments on synthetic and real data demonstrate its efficiency, accuracy, and robustness, particularly under high noise conditions. Furthermore, our nonminimal formulation ensures numerical stability, making it effective for real-world applications.

About the Speaker

Chen Lin was a Research Fellow at the Simons Foundation, where she specialized in 3D computer vision and visual(-inertial) SLAM. Her research spans from classical multiview geometry to learning-based pose estimation and scene understanding. Her ICCV 2025 paper introduces a new framework for rotation and pose estimation built on advanced algebraic paradigms.

Welcome to the Best of ICCV series, your virtual pass to some of the groundbreaking research, insights, and innovations that defined this year’s conference. Live streaming from the authors to you.

Date, Time and Location

Nov 21, 2025 9 AM Pacific Online. Register for the Zoom!

GECO: Geometrically Consistent Embedding with Lightspeed Inference

Recent advances in feature learning have shown that self-supervised vision foundation models can capture semantic correspondences but often lack awareness of underlying 3D geometry. GECO addresses this gap by producing geometrically coherent features that semantically distinguish parts based on geometry (e.g., left/right eyes, front/back legs). We propose a training framework based on optimal transport, enabling supervision beyond keypoints, even under occlusions and disocclusions. With a lightweight architecture, GECO runs at 30 fps, 98.2% faster than prior methods, while achieving state-of-the-art performance on PFPascal, APK, and CUB, improving PCK by 6.0%, 6.2%, and 4.1%, respectively. Finally, we show that PCK alone is insufficient to capture geometric quality and introduce new metrics and insights for more geometry-aware feature learning

About the Speaker

Regine Hartwig is a PHD Graduate Student at the Technical University of Munich

Proactive Comorbidity Prediction in HIV: Towards Fair and Trustworthy Care

HIV is a chronic infection that weakens the immune system and exposes patients to a high burden of comorbidities. While antiretroviral therapy has improved life expectancy, comorbidities remain a major challenge, and traditional screening protocols often fail to capture subtle risk patterns early enough. To address this, we develop a novel method trained on lab tests and demographic data from 2,200 patients in SE London. The method integrates feature interaction modeling, attention mechanisms, residual fusion and label-specific attention heads, outperforming TabNet, MLPs and classical machine learning models.

Our experiments show that incorporating demographic information improves predictive performance, though demographic recoverability analyses reveal that age and gender can still be inferred from lab data alone, raising fairness concerns. Finally, robustness checks confirm stable feature importance across cross-validation folds, reinforcing the trustworthiness of our approach.

About the Speaker

Dimitrios Kollias is an Associate Professor in Multimodal AI at Queen Mary University of London, specializing in machine/deep learning, trustworthy AI, computer vision, medical imaging & healthcare, behavior analysis, HMI. I have published 80+ papers (h-index 39; 6100+ citations) in top venues (e.g., CVPR, ICCV, ECCV, AAAI, IJCV, ECAI), invented a patent in behavior analysis (Huawei) and my research is widely adopted by academia and industry. I also serve as AI consultant and advisor to global companies, and have played leading roles in major international AI workshops and competitions.

Toward Trustworthy Embodied Agents: From Individuals to Teams

Modern intelligent embodied agents, such as service robots and autonomous vehicles, interact frequently with humans in dynamic, uncertain environments. They may also collaborate with each other as a team through effective communication to enhance task success, safety, and efficiency. These brings a few significant challenges. First, building reliable agents that safely navigate multi-agent scenarios requires scalable and generalizable prediction of surrounding agents’ behaviors and robust decision making under environmental uncertainty in out-of-distribution (OOD) scenarios. Second, effective cooperation between agents requires efficient communication and information fusion strategies and reliable task planning for complex long-horizon tasks.

In this talk, I will introduce a series of our recent work that addresses these challenges to enable safe and trustworthy embodied agents and their application to autonomous driving and service robots. Specifically, I will first demonstrate principled uncertainty quantification techniques and how they enable generalizable prediction and planning in out-of-distribution scenarios. Then, I will talk about effective approaches to enable efficient multi-agent communication and cooperation in centralized and decentralized settings.

About the Speaker

Dr. Jiachen Li is an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) and a cooperating faculty in the Department of Computer Science and Engineering (CSE) at the University of California, Riverside. He is the Director of the Trustworthy Autonomous Systems Laboratory and is affiliated with the Riverside Artificial Intelligence Research Institute (RAISE), the Center for Robotics and Intelligent Systems (CRIS), and the Center for Environmental Research and Technology (CE-CERT).

DRaM-LHM: A Quaternion Framework for Iterative Camera Pose Estimation

We explore a quaternion adjugate matrix-based representation for rotational motion in the Perspective-n-Point (PnP) problem. Leveraging quadratic quaternion terms within a Determinant Ratio Matrix (DRaM) estimation framework, we extend its application to perspective scenarios, providing a robust and efficient initialization for iterative PnP pose estimation. Notably, by solving the orthographic projection least-squares problem, DRaM provides a reliable initialization that enhances the accuracy and stability of iterative PnP solvers. Experiments on synthetic and real data demonstrate its efficiency, accuracy, and robustness, particularly under high noise conditions. Furthermore, our nonminimal formulation ensures numerical stability, making it effective for real-world applications.

About the Speaker

Chen Lin was a Research Fellow at the Simons Foundation, where she specialized in 3D computer vision and visual(-inertial) SLAM. Her research spans from classical multiview geometry to learning-based pose estimation and scene understanding. Her ICCV 2025 paper introduces a new framework for rotation and pose estimation built on advanced algebraic paradigms.

Welcome to the Best of ICCV series, your virtual pass to some of the groundbreaking research, insights, and innovations that defined this year’s conference. Live streaming from the authors to you.

Date, Time and Location

Nov 21, 2025 9 AM Pacific Online. Register for the Zoom!

GECO: Geometrically Consistent Embedding with Lightspeed Inference

Recent advances in feature learning have shown that self-supervised vision foundation models can capture semantic correspondences but often lack awareness of underlying 3D geometry. GECO addresses this gap by producing geometrically coherent features that semantically distinguish parts based on geometry (e.g., left/right eyes, front/back legs). We propose a training framework based on optimal transport, enabling supervision beyond keypoints, even under occlusions and disocclusions. With a lightweight architecture, GECO runs at 30 fps, 98.2% faster than prior methods, while achieving state-of-the-art performance on PFPascal, APK, and CUB, improving PCK by 6.0%, 6.2%, and 4.1%, respectively. Finally, we show that PCK alone is insufficient to capture geometric quality and introduce new metrics and insights for more geometry-aware feature learning

About the Speaker

Regine Hartwig is a PHD Graduate Student at the Technical University of Munich

Proactive Comorbidity Prediction in HIV: Towards Fair and Trustworthy Care

HIV is a chronic infection that weakens the immune system and exposes patients to a high burden of comorbidities. While antiretroviral therapy has improved life expectancy, comorbidities remain a major challenge, and traditional screening protocols often fail to capture subtle risk patterns early enough. To address this, we develop a novel method trained on lab tests and demographic data from 2,200 patients in SE London. The method integrates feature interaction modeling, attention mechanisms, residual fusion and label-specific attention heads, outperforming TabNet, MLPs and classical machine learning models.

Our experiments show that incorporating demographic information improves predictive performance, though demographic recoverability analyses reveal that age and gender can still be inferred from lab data alone, raising fairness concerns. Finally, robustness checks confirm stable feature importance across cross-validation folds, reinforcing the trustworthiness of our approach.

About the Speaker

Dimitrios Kollias is an Associate Professor in Multimodal AI at Queen Mary University of London, specializing in machine/deep learning, trustworthy AI, computer vision, medical imaging & healthcare, behavior analysis, HMI. I have published 80+ papers (h-index 39; 6100+ citations) in top venues (e.g., CVPR, ICCV, ECCV, AAAI, IJCV, ECAI), invented a patent in behavior analysis (Huawei) and my research is widely adopted by academia and industry. I also serve as AI consultant and advisor to global companies, and have played leading roles in major international AI workshops and competitions.

Toward Trustworthy Embodied Agents: From Individuals to Teams

Modern intelligent embodied agents, such as service robots and autonomous vehicles, interact frequently with humans in dynamic, uncertain environments. They may also collaborate with each other as a team through effective communication to enhance task success, safety, and efficiency. These brings a few significant challenges. First, building reliable agents that safely navigate multi-agent scenarios requires scalable and generalizable prediction of surrounding agents’ behaviors and robust decision making under environmental uncertainty in out-of-distribution (OOD) scenarios. Second, effective cooperation between agents requires efficient communication and information fusion strategies and reliable task planning for complex long-horizon tasks.

In this talk, I will introduce a series of our recent work that addresses these challenges to enable safe and trustworthy embodied agents and their application to autonomous driving and service robots. Specifically, I will first demonstrate principled uncertainty quantification techniques and how they enable generalizable prediction and planning in out-of-distribution scenarios. Then, I will talk about effective approaches to enable efficient multi-agent communication and cooperation in centralized and decentralized settings.

About the Speaker

Dr. Jiachen Li is an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) and a cooperating faculty in the Department of Computer Science and Engineering (CSE) at the University of California, Riverside. He is the Director of the Trustworthy Autonomous Systems Laboratory and is affiliated with the Riverside Artificial Intelligence Research Institute (RAISE), the Center for Robotics and Intelligent Systems (CRIS), and the Center for Environmental Research and Technology (CE-CERT).

DRaM-LHM: A Quaternion Framework for Iterative Camera Pose Estimation

We explore a quaternion adjugate matrix-based representation for rotational motion in the Perspective-n-Point (PnP) problem. Leveraging quadratic quaternion terms within a Determinant Ratio Matrix (DRaM) estimation framework, we extend its application to perspective scenarios, providing a robust and efficient initialization for iterative PnP pose estimation. Notably, by solving the orthographic projection least-squares problem, DRaM provides a reliable initialization that enhances the accuracy and stability of iterative PnP solvers. Experiments on synthetic and real data demonstrate its efficiency, accuracy, and robustness, particularly under high noise conditions. Furthermore, our nonminimal formulation ensures numerical stability, making it effective for real-world applications.

About the Speaker

Chen Lin was a Research Fellow at the Simons Foundation, where she specialized in 3D computer vision and visual(-inertial) SLAM. Her research spans from classical multiview geometry to learning-based pose estimation and scene understanding. Her ICCV 2025 paper introduces a new framework for rotation and pose estimation built on advanced algebraic paradigms.

Welcome to the Best of ICCV series, your virtual pass to some of the groundbreaking research, insights, and innovations that defined this year’s conference. Live streaming from the authors to you.

Date, Time and Location

Nov 21, 2025 9 AM Pacific Online. Register for the Zoom!

GECO: Geometrically Consistent Embedding with Lightspeed Inference

Recent advances in feature learning have shown that self-supervised vision foundation models can capture semantic correspondences but often lack awareness of underlying 3D geometry. GECO addresses this gap by producing geometrically coherent features that semantically distinguish parts based on geometry (e.g., left/right eyes, front/back legs). We propose a training framework based on optimal transport, enabling supervision beyond keypoints, even under occlusions and disocclusions. With a lightweight architecture, GECO runs at 30 fps, 98.2% faster than prior methods, while achieving state-of-the-art performance on PFPascal, APK, and CUB, improving PCK by 6.0%, 6.2%, and 4.1%, respectively. Finally, we show that PCK alone is insufficient to capture geometric quality and introduce new metrics and insights for more geometry-aware feature learning

About the Speaker

Regine Hartwig is a PHD Graduate Student at the Technical University of Munich

Proactive Comorbidity Prediction in HIV: Towards Fair and Trustworthy Care

HIV is a chronic infection that weakens the immune system and exposes patients to a high burden of comorbidities. While antiretroviral therapy has improved life expectancy, comorbidities remain a major challenge, and traditional screening protocols often fail to capture subtle risk patterns early enough. To address this, we develop a novel method trained on lab tests and demographic data from 2,200 patients in SE London. The method integrates feature interaction modeling, attention mechanisms, residual fusion and label-specific attention heads, outperforming TabNet, MLPs and classical machine learning models.

Our experiments show that incorporating demographic information improves predictive performance, though demographic recoverability analyses reveal that age and gender can still be inferred from lab data alone, raising fairness concerns. Finally, robustness checks confirm stable feature importance across cross-validation folds, reinforcing the trustworthiness of our approach.

About the Speaker

Dimitrios Kollias is an Associate Professor in Multimodal AI at Queen Mary University of London, specializing in machine/deep learning, trustworthy AI, computer vision, medical imaging & healthcare, behavior analysis, HMI. I have published 80+ papers (h-index 39; 6100+ citations) in top venues (e.g., CVPR, ICCV, ECCV, AAAI, IJCV, ECAI), invented a patent in behavior analysis (Huawei) and my research is widely adopted by academia and industry. I also serve as AI consultant and advisor to global companies, and have played leading roles in major international AI workshops and competitions.

Toward Trustworthy Embodied Agents: From Individuals to Teams

Modern intelligent embodied agents, such as service robots and autonomous vehicles, interact frequently with humans in dynamic, uncertain environments. They may also collaborate with each other as a team through effective communication to enhance task success, safety, and efficiency. These brings a few significant challenges. First, building reliable agents that safely navigate multi-agent scenarios requires scalable and generalizable prediction of surrounding agents’ behaviors and robust decision making under environmental uncertainty in out-of-distribution (OOD) scenarios. Second, effective cooperation between agents requires efficient communication and information fusion strategies and reliable task planning for complex long-horizon tasks.

In this talk, I will introduce a series of our recent work that addresses these challenges to enable safe and trustworthy embodied agents and their application to autonomous driving and service robots. Specifically, I will first demonstrate principled uncertainty quantification techniques and how they enable generalizable prediction and planning in out-of-distribution scenarios. Then, I will talk about effective approaches to enable efficient multi-agent communication and cooperation in centralized and decentralized settings.

About the Speaker

Dr. Jiachen Li is an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) and a cooperating faculty in the Department of Computer Science and Engineering (CSE) at the University of California, Riverside. He is the Director of the Trustworthy Autonomous Systems Laboratory and is affiliated with the Riverside Artificial Intelligence Research Institute (RAISE), the Center for Robotics and Intelligent Systems (CRIS), and the Center for Environmental Research and Technology (CE-CERT).

DRaM-LHM: A Quaternion Framework for Iterative Camera Pose Estimation

We explore a quaternion adjugate matrix-based representation for rotational motion in the Perspective-n-Point (PnP) problem. Leveraging quadratic quaternion terms within a Determinant Ratio Matrix (DRaM) estimation framework, we extend its application to perspective scenarios, providing a robust and efficient initialization for iterative PnP pose estimation. Notably, by solving the orthographic projection least-squares problem, DRaM provides a reliable initialization that enhances the accuracy and stability of iterative PnP solvers. Experiments on synthetic and real data demonstrate its efficiency, accuracy, and robustness, particularly under high noise conditions. Furthermore, our nonminimal formulation ensures numerical stability, making it effective for real-world applications.

About the Speaker

Chen Lin was a Research Fellow at the Simons Foundation, where she specialized in 3D computer vision and visual(-inertial) SLAM. Her research spans from classical multiview geometry to learning-based pose estimation and scene understanding. Her ICCV 2025 paper introduces a new framework for rotation and pose estimation built on advanced algebraic paradigms.

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

Date and Location

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

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

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

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

About the Speaker

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

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

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

About the Speaker

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

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

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

About the Speaker

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

Language Diffusion Models

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

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

About the Speaker

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

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

Date and Location

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

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

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

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

About the Speaker

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

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

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

About the Speaker

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

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

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

About the Speaker

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

Language Diffusion Models

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

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

About the Speaker

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

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

Date and Location

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

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

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

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

About the Speaker

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

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

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

About the Speaker

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

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

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

About the Speaker

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

Language Diffusion Models

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

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

About the Speaker

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

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

Date and Location

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

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

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

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

About the Speaker

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

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

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

About the Speaker

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

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

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

About the Speaker

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

Language Diffusion Models

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

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

About the Speaker

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