Join our in-person meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. [Register to reserve your spot.](https://voxel51.com/events/nyc-physical-ai-ml-and-computer-vision-meetup-november-13-2026)[ ](https://voxel51.com/events/nyc-physical-ai-ml-and-computer-vision-meetup-november-13-2026)Seats are limited. Date, Time and Location Nov 13, 2026 5:30 PM - 8:30 PM EST Fractal, 111 Conselyea St, Fl 2, Brooklyn, NY 11211 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](https://www.linkedin.com/in/irmak-guzey-6a9010175) is a rising 3rd year PhD student at New York University, currently advised by Lerrel Pinto. Her research focuses on robot learning for dexterous manipulation. She has been awarded a Fulbright scholarship and NYU's Best Master's Thesis Award. Beyond Frames: Searching Hours of Footage with One Natural-Language Query 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 fi
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