London AI, Machine Learning and Computer Vision Meetup

Join our virtual meetup to hear talks from experts on cutting-edge topics across Visual AI for Physical AI use cases.

Date, Time and Location

Dec 11, 2025
9:00-11:00 AM Pacific
Online. Register for the Zoom!

From Data to Open-World Autonomous Driving

Data is key for advances in machine learning, including mobile applications like robots and autonomous cars. To ensure reliable operation, occurring scenarios must be reflected by the underlying dataset. Since the open-world environments can contain unknown scenarios and novel objects, active learning from online data collection and handling of unknowns is required. In this talk we discuss different approach to address this real world requirements.

About the Speaker

Sebastian Schmidt is a PhD student at the Data Analytics and Machine Learning group at TU Munich and part of an Industrial PhD Program with the BMW research group. His work is mainly focused on Open-world active learning and perception for autonomous vehicles.

From Raw Sensor Data to Reliable Datasets: Physical AI in Practice

Modern mobility systems rely on massive, high-quality multimodal datasets — yet real-world data is messy. Misaligned sensors, inconsistent metadata, and uneven scenario coverage can slow development and lead to costly model failures. The Physical AI Workbench, built in collaboration between Voxel51 and NVIDIA, provides an automated and scalable pipeline for auditing, reconstructing, and enriching autonomous driving datasets.

In this talk, we’ll show how FiftyOne serves as the central interface for inspecting and validating sensor alignment, scene structure, and scenario diversity, while NVIDIA Neural Reconstruction (NuRec) enables physics-aware reconstruction directly from real-world captures. We’ll highlight how these capabilities support automated dataset quality checks, reduce manual review overhead, and streamline the creation of richer datasets for model training and evaluation.

Attendees will gain insight into how Physical AI workflows help mobility teams scale, improve dataset reliability, and accelerate iteration from data capture to model deployment — without rewriting their infrastructure.

About the Speaker

Daniel Gural leads technical partnerships at Voxel51, where he’s building the Physical AI Workbench, a platform that connects real-world sensor data with realistic simulation to help engineers better understand, validate, and improve their perception systems. With a background in developer relations and computer vision engineering,

Building Smarter AV Simulation with Neural Reconstruction and World Models

This talk explores how neural reconstruction and world models are coming together to create richer, more dynamic simulation for scalable autonomous vehicle development. We’ll look at the latest releases in 3D Gaussian splatting techniques and world reasoning and generation, as well as discuss how these technologies are advancing the deployment of autonomous driving stacks that can generalize to any environment. We’ll also cover NVIDIA open models, frameworks, and data to help kickstart your own development pipelines.

About the Speaker

Katie Washabaugh is NVIDIA’s Product Marketing Manager for Autonomous Vehicle Simulation, focusing on virtual solutions for real world mobility. A former journalist at publications such as Automotive News and MarketWatch, she joined the NVIDIA team in 2018 as Automotive Content Marketing Manager. Katie holds a B.A. in public policy from the University of Michigan and lives in Detroit.

Relevance of Classical Algorithms in Modern Autonomous Driving Architectures

While modern autonomous driving systems increasingly rely on machine learning and deep neural networks, classical algorithms continue to play a foundational role in ensuring reliability, interpretability, and real-time performance. Techniques such as Kalman filtering, A* path planning, PID control, and SLAM remain integral to perception, localization, and decision-making modules. Their deterministic nature and lower computational overhead make them especially valuable in safety-critical scenarios and resource-constrained environments. This talk explores the enduring relevance of classical algorithms, their integration with learning-based methods, and their evolving scope in the context of next-generation autonomous vehicle architectures.

Prajwal Chinthoju is an Autonomous Driving Feature Development Engineer with a strong foundation in systems engineering, optimization, and intelligent mobility. I specialize in integrating classical algorithms with modern AI techniques to enhance perception, planning, and control in autonomous vehicle platforms.

This hands-on workshop introduces you to the Physical AI Workbench, a new layer of FiftyOne designed for autonomous vehicle, robotics, and 3D vision workflows. You’ll learn how to bridge the gap between raw sensor data and production-quality datasets, all from within FiftyOne’s interactive interface.

Date, Time and Location

Dec 16, 2025
9:00-10:00 AM Pacific
Online. Register for the Zoom!

Through live demos, you’ll explore how to:

• Audit: Automatically detect calibration errors, timestamp misalignments, incomplete frames, and other integrity issues that arise from dataset format drift over time.
• Generate: Reconstruct and augment your data using NVIDIA pathways such as NuRec, COSMOS, and Omniverse, enabling realistic scene synthesis and physical consistency checks.
• Enrich: Integrate auto-labeling, embeddings, and quality scoring pipelines to enhance metadata and accelerate model training.
• Export and Loop Back: Seamlessly export to and re-import from interoperable formats like NCore to verify consistency and ensure round-trip fidelity.

You’ll gain hands-on experience with a complete physical AI dataset lifecycle—from ingesting real-world AV datasets like nuScenes and Waymo, to running 3D audits, projecting LiDAR into image space, and visualizing results in FiftyOne’s UI. Along the way, you’ll see how Physical AI Workbench automatically surfaces issues in calibration, projection, and metadata—helping teams prevent silent data drift and ensure reliable dataset evolution.

By the end, you’ll understand how the Physical AI Workbench standardizes the process of building calibrated, complete, and simulation-ready datasets for the physical world.

Who should attend

Data scientists, AV/ADAS engineers, robotics researchers, and computer vision practitioners looking to standardize and scale physical-world datasets for model development and simulation.

About the Speaker

Daniel Gural leads technical partnerships at Voxel51, where he’s building the Physical AI Workbench, a platform that connects real-world sensor data with realistic simulation to help engineers better understand, validate, and improve their perception systems.

This technical workshop focuses on the data infrastructure required to build and maintain production-grade mobility datasets at fleet scale.

Date, Time and Location

Jan 14, 2026
9:00-10:00 AM Pacific
Online. Register for the Zoom!

We will examine how to structure storage, metadata, access patterns, and quality controls so that mobility teams can treat perception datasets as first-class, versioned “infrastructure” assets. The session will walk through how to design a mobility data stack that connects object storage, labeling systems, simulation environments, and experiment tracking into a coherent, auditable pipeline.

What you’ll learn:

• Model the mobility data plane: Define schemas for camera, LiDAR, radar, and HD, and represent temporal windows, ego poses, and scenario groupings in a way that is queryable and stable under schema evolution.
• Build a versioned dataset catalog with FiftyOne: Use FiftyOne customized workspaces and views to represent canonical datasets, and integrate with your raw data sources. All while preserving lineage between raw logs, the curated data, and simulation inputs.
• Implement governance and access control on mobility data: Configure role-based access and auditable pipelines to enforce data residency constraints while encouraging multi-team collaboration across research, perception, and safety functions.
• Operationalize curation and scenario mining workflows: Use FiftyOne’s embeddings and labeling capabilities to surface rare events such as adverse weather and sensor anomalies. Assign review tasks, and codify “critical scenario” definitions as reproducible dataset views.
• Close the loop with evaluation and feedback signals: Connect FiftyOne to training and evaluation pipelines so that model failures feed back into dataset updates

By the end of the workshop, attendees will have a concrete mental model and reference architecture for treating mobility datasets as a governed, queryable, and continuously evolving layer in their stack.

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

Jan 14, 2025
9 AM Pacific
Online. Register for the Zoom!

EgoExOR: An Ego-Exo-Centric Operating Room Dataset for Surgical Activity Understanding

Operating rooms (ORs) demand precise coordination among surgeons, nurses, and equipment in a fast-paced, occlusion-heavy environment, necessitating advanced perception models to enhance safety and efficiency. Existing datasets either provide partial egocentric views or sparse exocentric multi-view context, but do not explore the comprehensive combination of both. We introduce EgoExOR, the first OR dataset and accompanying benchmark to fuse first-person and third-person perspectives. Spanning 94 minutes (84,553 frames at 15 FPS) of two emulated spine procedures, Ultrasound-Guided Needle Insertion and Minimally Invasive Spine Surgery,

EgoExOR integrates egocentric data (RGB, gaze, hand tracking, audio) from wearable glasses, exocentric RGB and depth from RGB-D cameras, and ultrasound imagery. Its detailed scene graph annotations, covering 36 entities and 22 relations (568,235 triplets), enable robust modeling of clinical interactions, supporting tasks like action recognition and human-centric perception. We evaluate the surgical scene graph generation performance of two adapted state-of-the-art models and offer a new baseline that explicitly leverages EgoExOR's multimodal and multi-perspective signals. This new dataset and benchmark set a new foundation for OR perception, offering a rich, multimodal resource for next-generation clinical perception.

About the Speaker

Ege Özsoy is a last year PhD student researching multimodal computer vision and vision–language models for surgical scene understanding, focusing on semantic scene graphs, multimodality, and ego-exocentric modeling in operating rooms.

SANSA: Unleashing the Hidden Semantics in SAM2 for Few-Shot Segmentation

Few-shot segmentation requires recognizing novel object categories from only a few annotated examples, demanding both accurate mask generation and strong visual correspondence. While Segment Anything 2 (SAM2) provides powerful prompt-based segmentation and built-in feature matching, its representations are entangled with tracking-specific cues that limit higher-level semantic generalization. We show that SAM2 nonetheless encodes rich latent semantic structure despite its class-agnostic training. To leverage this, we introduce SANSA, a lightweight framework that makes this structure explicit and adapts SAM2 for few-shot segmentation with minimal modifications. SANSA achieves state-of-the-art generalization performance, outperforms generalist in-context methods, supports flexible prompting, and remains significantly faster and smaller than prior approaches.

About the Speaker

Claudia Cuttano is a PhD student in the VANDAL Lab at Politecnico di Torino and is currently conducting a research visit at TU Darmstadt with Prof. Stefan Roth in the Visual Inference Lab. Her work centers on semantic segmentation, particularly on multi-modal scene understanding and leveraging foundation models for pixel-level vision tasks.

Nested Learning: The Illusion of Deep Learning Architectures

We present Nested Learning (NL), a new learning paradigm for continual learning that views machine learning models and their training process as a set of nested and/or parallel optimization problems, each of which with its own context flow, frequency of update, and learning algorithm. Based on NL, we design a new architecture, called Hope, that is capable of continual learning and also modifying itself, if it is needed.

About the Speaker

Ali Behrouz is a Ph.D. student in the Computer Science Department at Cornell University and a research intern at Google Research. His research spans topics from deep learning architectures to continual learning and neuroscience, and appeared at NeurIPS, ICML, KDD, WWW, CHIL, VLDB, ... conferences. His work has been featured with two Best Paper awards, a Best Paper Honorable Mention award, a Best Paper Award candidate, and oral and spotlight presentations.

Are VLM Explanations Faithful? A Counterfactual Testing Approach

VLMs sound convincing—but are their explanations actually true? This talk introduces Explanation-Driven Counterfactual Testing (EDCT), a simple and model-agnostic method that evaluates whether VLM explanations align with the evidence models truly use. By perturbing the very features a model claims to rely on, EDCT exposes mismatches between stated reasoning and real decision pathways. I will show surprising failure cases across state-of-the-art VLMs and highlight how EDCT can guide more trustworthy explanation methods.

About the Speaker

Santosh Vasa is a Machine Learning Engineer at Mercedes-Benz R&D North America, working on multimodal perception and VLM safety for autonomous driving. He co-authored the EDCT framework and focuses on explainability, counterfactual testing, and trustworthy AI.