London AI, Machine Learning and Computer Vision Meetup

Aug 25, 2026
9:00 AM - 11:00 AM PST
Online. Register for the Zoom!

Using Computer Vision to Advance the Sciences

I'll present some of our ongoing work on using computer vision to create impact in the sciences. These target a two areas, solar physics and evolutionary biology, that deal with objects of radically different sizes but are unified by a need for high quality, trustworthy data.

I'll show off our efforts, done in collaboration with domain experts, that aim to produce the best possible maps of the Sun's powerful magnetic field and have created some of the world's largest repositories of data about bird morphology.

About the Speaker

David Fouhey is an Associate Professor at New York University and a research scientist at Polymathic AI. Before joining NYU, he received a PhD in robotics from Carnegie Mellon, was a postdoc at UC Berkeley, and was a professor at University of Michigan.

Solaris: Building a Multiplayer Video World Model in Minecraft

This talk will introduce Solaris: a multiplayer video world model in Minecraft. I will first present SolarisEngine, the software platform we built to simulate realistic multiplayer gameplay between bots at scale, enabling us to collect a large training dataset of aligned multiplayer actions and frames.

I will then discuss our staged training pipeline, starting with single-player pre-training before converting the model into a long-horizon multiplayer generator through bidirectional training, followed by causal training, and concluding with Self Forcing. I will also cover our memory-efficient implementation of Self Forcing, called Checkpointed Self Forcing.

Finally, I will showcase generated videos illustrating how Solaris maintains coherent long-horizon multiplayer interactions.

About the Speaker

Oscar Michel is a PhD student at NYU advised by Prof. Saining Xie. His research studies world models: generative models of agents interacting in an environment.

Closing the human to robot gap for dexterous hands

Collecting task-specific robot data for multi-fingered hands is challenging due to the many difficulties that arise in teleoperation. That is why recently there has been a major focus on learning robot policies directly from human demonstrations. However, human demonstrations are difficult to work with; there is a major morphological and visual gap between human and robot hands, as well as between the environments they operate in.

In this talk, I'd like to discuss my efforts on closing this gap.

About the Speaker

Irmak Guzey I'm Irmak (she/her), a rising 3rd year PhD student at New York University, currently advised by Lerrel Pinto. My research focuses on robot learning for dexterous manipulation. I have been awarded a Fulbright scholarship and NYU's Best Master's Thesis Award in the past.

Aug 27, 2026
9:00 AM - 11:00 AM PST
Online. Register for the Zoom!

Robust Concept Protection against Diffusion-Based Image Editing and Personalization

Diffusion-based image editing and personalization models have made it increasingly easy to manipulate and replicate visual concepts from only a few reference images. However, existing protection methods often overfit to a single attack model and fail to generalize across diverse editing pipelines.

In this presentation, I will discuss recent advances in concept protection for generative AI systems, focusing on targeted perturbation strategies and style-sensitive diffusion representations. I will also present experimental findings across multiple editing and fine-tuning scenarios, highlighting the challenges of robustness, transferability, and imperceptibility in practical protection settings. Finally, I will discuss open problems and future directions toward trustworthy generative content ownership.

About the Speaker

Qiuyu Tang is a Ph.D. student in Computer Science and Engineering at Lehigh University. Her research focuses on trustworthy AI, media forensics, and robust protection methods against diffusion-based image editing and personalization systems. Her recent work explores concept protection, style safeguarding, semantic image manipulation, and generative AI robustness. She has contributed to multiple publications in computer vision and AI safety, including research on diffusion model protection and manipulation detection, and has also served as a conference workshop organizer.

From Pixels to the Planet: Building Scalable and Grounded AI for Science

AI has demonstrated a lot of new possibilities, from drafting emails to image editing and generation. The efficacy of AI models is largely built upon a standard machine learning pipeline, where data is fed into models to get representations and predictions, and the performance is evaluated with controlled benchmarks and metrics. However, the mismatch arises when we try to transit this pipeline to the interaction with the real world and use AI for scientific discovery. Beyond close-set decisions, scientists want to discover new categories and propose new hypotheses. In this talk, I will share how I address the challenges of AI for science from the perspectives of data-centric methods and interpretability approaches.

About the Speaker

Jianyang Gu is a postdoctoral scholar at The Ohio State University. His research focuses on using data-centric methods to build scalable and interpretable foundation models for science.

Building Real-World Computer Vision Systems with Voxel51

This talk will explore practical workflows for building, evaluating, and improving modern computer vision systems. We’ll dive into real-world approaches to dataset curation, model analysis, multimodal AI workflows, and production-ready vision pipelines using open-source technologies.

The session is designed for engineers, researchers, and AI practitioners looking to better understand how teams are developing and scaling computer vision applications today. Expect practical demos, technical insights, and discussions around the evolving AI tooling ecosystem.

About the Speaker

Daniel Gural is an expert in Physical AI and has been working in the field for over 8 years. Working across healthcare he has experience in both operating use case as well as using Visual AI as an aid in psychology applications as well.

Seeing Is Not Enough: Visual Grounding, World Models and Why Computer-Use Agents Fail at Step 17

Computer-use agents are a computer vision problem wearing an agent costume: the model has to read a screen it has never seen, locate the right control among hundreds of near-identical ones, and understand what changed after it clicked — thirty times in a row without drifting. This talk covers the two things we built at Obin AI to make that work: a video-based world model that learns the latent dynamics of GUI and document environments, so the agent can roll out a candidate workflow internally and pick the branch that doesn't dead-end, and a checkpoint-based evaluation harness that scores verifiable intermediate states rather than required action sequences.

I'll be specific about the vision side — what the model needs to represent about an interface and what it can safely ignore, why optimising for target-state reachability beat optimising for reconstruction fidelity, and how we separate visual grounding failures from planning failures, since they look identical in the logs and need completely different fixes. The arithmetic that motivates all of it: at 90% per-step reliability a thirty-step workflow completes 4% of the time, which is why per-step benchmarks flatter these systems so badly.

I'll also cover what didn't work — deep rollouts against an imperfect dynamics model, and step-level preference data that produced more plausible-looking actions and fewer completed tasks.

About the Speaker

Nevasini Sasikumar is a machine learning researcher at Obin AI, where she builds video-based world models and computer-use agents that read screens and operate desktop and web software autonomously, with finance back-office automation as the deployment domain.

In this hands-on workshop, you'll use FiftyOne and the High Quality Invoice Images for OCR dataset to run the full data-centric loop end-to-end: embed invoices with a modern visual document model, cluster them by structure, run LightOnOCR as your base model, and use per-sample evaluation scores layered onto embedding space to find *where* and *why* it fails.

Time, Date and Location

Sep 02, 2026
9:00 AM - 11:00 AM PST
Online. Register for the Zoom!

What You'll Walk Away With

• A working FiftyOne pipeline for any document collection you own
• A repeatable curation query that combines evaluation + embedding signals
• A fine-tuned LightOnOCR checkpoint that demonstrably outperforms the base model on your invoices
• The mental model that data curation — not architecture or hyperparameters — is the highest-leverage thing you can do to improve a document AI system

Sep 17, 2026
9:00 AM - 11:00 AM PST
Online. Register for the Zoom!

AI for Autonomous Driving: From Data to Decisions

Building reliable automated driving systems is as much a data and engineering challenge as a modeling one. In this talk, Tin will share perspectives from his work at Porsche AG on applying modern AI methods across the autonomous driving development process, from making sense of large-scale driving data to understanding and evaluating how AI-based systems behave on the road. He'll discuss lessons learned from real-world development, where today's approaches shine, and where hard problems remain for the ADAS and AV community.

About the Speaker

Tin Stribor Sohn is a PhD Student at Porsche AG and Karlsruhe Institute of Technology in the area of Foundation Models for Scenario Understanding and Decision Making in Autonomous Robotics, Tech Lead at Data Driven Engineering for Autonomous Driving, Prior: Master in CS at University of Tuebingen with focus on Computer Vision and Deep Learning and co-founder of a software company for smart EV charging

Advancing ADAS and Autonomous Vehicle Development with Multimodal Data

ADAS and autonomous vehicle systems rely on increasingly complex data from cameras, video, LiDAR, radar, and other sensor streams. In this session, Murilo will introduce Voxel51 and explore how the latest multimodal capabilities in FiftyOne help teams bring these data sources together to better understand their datasets and model behavior. He’ll discuss how unified workflows for visualization, search, curation, and evaluation can help ADAS and AV teams uncover challenging scenarios, investigate model failures, and build safer, more reliable autonomous systems.

About the Speaker

Murilo Gustineli is a Machine Learning Engineer at Voxel51 working at the intersection of representation learning and computer vision. He holds an M.S. in Computer Science from Georgia Tech, where he co-leads the DS@GT Applied Research & Competitions group, advancing machine learning research through competitive challenges and peer-reviewed publications.

From Survey-Grade Maps to Physical AI: Scaling Real-World Data for Training and Simulation

Physical AI systems are increasingly constrained not by model architectures, but by the availability of scalable, high-fidelity real-world data. This talk explores how Dynamic Map Platform transforms survey-grade road assets collected across 1.8 million km of roads worldwide into training- and simulation-ready datasets, including point clouds, imagery, HD maps, road surface models, and 3D Gaussian Splatting representations.

We will discuss why geometric accuracy, semantic understanding, and real-world diversity are critical to building robust autonomous driving systems. Attendees will learn how real-world geospatial data can be structured and scaled for AI training and simulation workflows.

About the Speaker

Ryoto Miyake is a Software Engineer at Dynamic Map Platform, where he works on transforming large-scale geospatial data into AI-ready datasets for training, simulation, and validation, such as HD maps and 3D Gaussian Splatting. With a background in transportation engineering, he works closely with automotive manufacturers and industry partners to bridge large-scale real-world mapping data with next-generation AI and mobility systems.