July 27 - London AI, ML, and Computer Vision Meetup
Details
Join our in-person meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.
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Date, Time and Location
Jul 27, 2026
5:30 PM - 8:30 PM BST
Imperial College London, Skempton Building (LT201), South Kensington, London SW7 2AZ
UniLight: Unified Multi-Modal Lighting Representation
Lighting has a strong influence on visual appearance, yet understanding and representing lighting in images remains notoriously difficult. UniLight introduces a joint latent space to unify previously incompatible lighting representation - environment maps, images, irradiance and text descriptions.
Modality-specific encoders are trained contrastively to align their representations, with an auxiliary spherical-harmonics prediction task reinforcing directional understanding. Our joint lighting embedding enables applications such as retrieval, example-based light control during image generation, and environment map generation from various modalities.
About the Speaker
Zitian Zhang - is a PhD candidate in Computer Science at Université Laval, and a research scientist intern in Adobe Research London. He focuses on image understanding, generation, and lighting representations through foundation models.
LoST: Level of Semantics Tokenization for 3D Shapes
Tokenization is fundamental to generative modeling and especially important for autoregressive 3D generation. However, current 3D shape tokenizers rely on geometric level-of-detail hierarchies that are token-inefficient and poorly aligned with semantic structure.
We propose Level-of-Semantics Tokenization (LoST), which orders tokens by semantic salience so early tokens produce complete, plausible shapes and later tokens refine detailed geometry and semantics. LoST is trained with Relational Inter-Distance Alignment (RIDA), a semantic alignment loss that matches relationships in 3D shape latent space to those in DINO feature space.
Experiments show that LoST achieves state-of-the-art reconstruction and efficient high-quality AR 3D generation while using only 0.1%–10% of the tokens required by prior methods.
About the Speaker
Niladri Dutt - is an ELLIS PhD student at University College London (UCL), sponsored by Adobe Research. He is advised by Prof Niloy Mitra (UCL) and Duygu Ceylan (Adobe).
Material selection in 2D and beyond - methods, tricks and applications
In this talk, we'll explore reasoning about images from a material-centric perspective, namely through the lens of material understanding. Materials distinguish themselves by their response to light, which is governed and modelled through physical properties like roughness or gloss - however, understanding such properties is a non-trivial task for current algorithms and models.
We'll see how we can select materials similar to a given query material, significantly improve selection fidelity and eventually even venture beyond 2D, to enable selection in the 3D domain.
About the Speaker
Michael Fischer - is a research scientist at Adobe research London. He obtained his PhD from University College London (UCL), advised by Niloy Mitra and Tobias Ritschel. Michael has authored several top-tier publications (CVPR, ICCV, SIGGRAPH, ...) and is a recipient of both the Meta PhD scholarship and the Rabin Ezra scholarship as well as the Eurographics PhD Thesis award 2026.
Lessons from the Trenches of Agentic Engineering
A candid lessons-learned from running an agentic engineering consultancy with clients ranging from federal governments to early-stage AI startups. I'll cover what's held up under real production pressure, what I tried and abandoned, and the approaches that are quietly dead but still being sold. Expect specifics, opinions, and a few uncomfortable conclusions.
About the Speaker
John Adeojo - runs Brainqub3 an agentic engineering consultancy serving clients from federal governments to early-stage AI startups, and recently served briefly as CTO of a pre-seed AI startup. He previously led the data science function at RBS International and held senior IC roles at HSBC, NatWest Group, and Shawbrook Bank.
Building Real-World Computer Vision Systems
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
Harpreet Sahota - is a hacker-in-residence and machine learning engineer with a passion for deep learning and generative AI. He’s got a deep interest in RAG, Agents, and Multimodal AI.
