London AI, Machine Learning and Computer Vision Meetup

Mar 11, 2026
10-11am PST
Online, Register for the Zoom!

Industries from robotics to autonomous vehicles are converging on world foundation models (WFMs) and action-conditioned video generation, where the challenge is predicting physics, causality, and intent. But this shift has created a massive new bottleneck: validation.

How do you debug a model that imagines the future? How do you curate petabyte-scale video datasets to capture the "long tail" of rare events without drowning in storage costs? And how do you ensure temporal consistency when your training data lives in scattered data lakes?

In this session, we explore technical workflows for the next generation of Visual AI. We will dissect the "Video Data Monster," demonstrating how to build feedback loops that bridge the gap between generative imagination and physical reality. Learn how leading teams are using federated data strategies and collaborative evaluation to turn video from a storage burden into a structured, queryable asset for embodied intelligence.

About the Speaker

Nick Lotz is chemical process engineer-turned-developer who is currently a Technical Marketing Engineer at Voxel51. He is particularly interested in bringing observability and security to all layers of the AI stack.

Mar 12, 2026
9 - 11 AM PST
Online. Register for the Zoom!

Agents Building Agents on the Hugging Face Hub

Discover how coding agents can run or support your fine-tuning experiments. From quick dataset validation and preprocessing, to optimal GPU hardware selection, to automated job submission based on metric tracking, to evaluation. Ben will demonstrate how Hugging Face skills can be used to define best practices for agents to support machine learning experiments. Bring Claude, Codex, or Mistral Vibes, and we’ll show you to get it training models with GRPO, SFT, and DPO.

About the Speaker

Ben Burtenshaw is a Machine Learning Engineer at Hugging Face, focusing on building agents with fine-tuning and reinforcement learning. He led educational projects like the Agents Course, the MCP Course, and the LLM course, which bridge the gap between complex Reinforcement Learning (RL) techniques and practical application. Ben focuses on democratizing access to efficient AI, empowering the community to align, evaluate, and deploy transparent agentic systems.

Claude Code Templates

This talk explores how to configure and align Claude Code agents using templates and custom components. I'll demonstrate practical configuration patterns that ensure your CLI agent executes exactly what you intend, covering Skills setup, hooks implementation, and template customization. Drawing from real-world examples building Claude Code Templates, attendees will learn how to structure their agent configurations for consistent, reliable behavior and create reusable components that maintain alignment across different use cases.

About the Speaker

Daniel Avila is an AI Engineer at Hedgineer building agentic systems and creator of Claude Code Templates.

Move Faster in Computer Vision by Teaching Agents to See Your Data

Computer vision teams spend too much time writing scripts just to find bad labels, blurry images, and edge cases. In this talk, I’ll show how to move that work to agents by using FiftyOne as a visual operating system. With Skills and MCP, agents can see inside your datasets, explore them visually, and handle common data cleanup tasks, so you can spend less time on data and more time shipping models.

About the Speaker

Adonai Vera - Machine Learning Engineer & DevRel at Voxel51. With over 7 years of experience building computer vision and machine learning models using TensorFlow, Docker, and OpenCV. I started as a software developer, moved into AI, led teams, and served as CTO. Today, I connect code and community to build open, production-ready AI, making technology simple, accessible, and reliable.

Skills As Documentation

Skills are self-contained recipes - each one a piece of a larger puzzle. Instead of trying to modify human-centric documentation to better fit agents, skills let us build capabilities into our agents directly. This talk will showcase how to think about leveraging skills to enhance how users interact with your software!

About the Speaker

Chris Alexiuk is a deep learning developer advocate at NVIDIA, working on creating technical assets that help developers use the incredible suite of AI tools available at NVIDIA. Chris comes from a machine learning and data science background, and he is obsessed with everything and anything about large language models.

Join us for an interactive workshop where we'll build production-ready computer vision pipelines using vibe coded FiftyOne plugins.

Register for the Zoom

Plugins enable you to customize the open-source FiftyOne computer vision app to match your exact workflow by easily incorporating data annotation, curation, model evaluation and inference.

We'll demonstrate how FiftyOne Skills and the MCP Server can streamline the journey from prototype to production-ready pipelines, keeping your development flow intact.

Perfect for open-source contributors, researchers, and enterprise teams seeking hands-on experience. All participants receive slides, notebooks, and access to GitHub repositories and videos from the workshop.

Mar 19, 2026
9 - 11 AM Pacific
Online. Register for Zoom!

Towards Reliable Clinical AI: Evaluating Factuality, Robustness, and Real-World Performance of Large Language Models

Large language models are increasingly deployed in clinical settings, but their reliability remains uncertain—they hallucinate facts, behave inconsistently across instruction phrasings, and struggle with evolving medical terminology. In my talk, I address methods to systematically evaluate clinical LLM reliability across four dimensions aligned with how healthcare professionals actually work: verifying concrete facts (FactEHR), ensuring stable guidance across instruction variations (instruction sensitivity study showing up to 0.6 AUROC variation), integrating up-to-date knowledge (BEACON improving biomedical NER by 15%), and assessing real patient conversations (PATIENT-EVAL revealing models abandon safety warnings when patients seek reassurance). These contributions establish evaluation standards spanning factuality, robustness, knowledge integration, and patient-centered communication, charting a path toward clinical AI that is safer, more equitable, and more trustworthy.

About the Speaker

Monica Munnangi is a doctoral student at the Khoury College of Computer Sciences at Northeastern University, advised by Saiph Savage. Her doctoral research, which she began in 2021 and expects to complete in 2026, focuses on multi-modal machine learning for healthcare. After being introduced to artificial intelligence and machine learning during her undergraduate studies, Munnangi earned her master’s degree from UMass Amherst.

Neural BRDFs: Learning Compact Representations for Material Appearance

Accurately modeling how light interacts with real-world materials remains a central challenge in rendering. Bidirectional Reflectance Distribution Functions (BRDFs) describe how materials reflect light as a function of viewing and lighting directions. Creating realistic digital materials has traditionally required choosing between fast parametric models that can't capture real-world complexity, or massive measured BRDFs that are expensive to acquire and store. Neural BRDFs address this challenge by learning continuous reflectance functions from data, exploiting directional correlations and symmetry to achieve significant compression while maintaining rendering quality. In this talk, we examine how neural networks can encode complex material behavior compactly, why this matters for rendering and material capture, and how neural BRDFs fit into the broader evolution toward data-driven graphics.

About the Speaker

Manushree Gangwar is a Machine Learning Engineer at Voxel51 working on data-centric visual AI. She holds an MS in Computer Science from Columbia University and has previously worked in robotics, autonomous driving, and AR/VR, with a focus on scene understanding and 3D reconstruction.

Supercharging AI agents with evaluations

Reliable deployment of AI agents depends on rigorous evaluation, which must shift from a nice-to-have QA step to a core engineering discipline. Robust evaluation is essential for safety, predictability, misuse resistance, and sustained user trust. To meet this bar, Evals must be deeply integrated into the agent development lifecycle. This talk will outline how simulation-based testing—using high-fidelity, controllable environments—provides the next generation of evaluation infrastructure for production-ready AI agents.

About the Speaker

Priya Venkat, PhD, is a Senior AI Manager at Intuit, where she leads teams that build and scale ML and Agentic AI systems for finance. Her work integrates cutting-edge agentic workflows and robust evaluation systems to drive business impact while ensuring AI safety and reliability. Priya is a strong advocate of responsible AI, and actively mentors the next generation of AI scientists and engineers.

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 a 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.