NYC Physical AI, ML, and Computer Vision Meetup - November 13, 2026

Join our in-person meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

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.

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.

Most computer vision pipelines still treat video as a pile of frames, which makes 'find the moment where X happens' surprisingly hard at archive scale. This lightning talk shows how video-native embeddings and video-language models change that, turning hours of footage into something you can search with a single natural-language query. I'll cover how video is segmented and embedded, how retrieval works across visual, audio and text signals, where temporal context gets lost, and the evaluation problems teams hit when there's no clean ground truth. The examples come from field work deploying video understanding for media, news, streaming and sports organizations. You'll leave with a mental model and a checklist for building video search that holds up beyond the demo.

Most embedding compression methods impose a predefined coding geometry, such as coordinate partitions, low-rank subspaces, or unrestricted codebooks. In this talk, I introduce OrBIT, a framework that instead learns reusable geometric structure from operator dynamics to guide the compression of large language model embeddings. Using orbit-generated dictionaries, shared additive codewords, and redundant overlapping representations, OrBIT combines local compression with global reconstruction, allowing errors across representations to compensate for one another. I will explain the mathematical principles behind this construction, its compact decoder, and experiments demonstrating up to 37.9× compression on GPT-2 embeddings and over 23× on several 7B-model embedding tables, with competitive rate–distortion performance. The talk will explore how learning representation geometry can open new directions for efficient and structure-aware machine learning.