Buffalo Physical AI Workshop and Meetup - November 9, 2026

Nov 09, 2026

5:30 PM - 8:30 PM EST

Room B2-205, UB Business & Entrepreneur Partnerships, 701 Ellicott Street, Buffalo, NY 14203

Speakers

About this event

Join us in Buffalo on November 9th for the Buffalo Physical AI Workshop and Meetup, co-presented by Nebius and Voxel51. We will be at the University at Buffalo Business & Entrepreneur Partnerships, 701 Ellicott Street, Room B2-205, on the Buffalo Niagara Medical Campus. This stop is co-sponsored by the University at Buffalo Business & Entrepreneur Partnerships (BEP).

The evening opens with networking, food, and drinks, followed by a hands-on workshop on building physical AI applications with FiftyOne, from curating robotics and sensor datasets to evaluating vision models on real-world data. Networking continues after the workshop.

A laptop is required to participate in the hands-on workshop - please bring one. Space is limited, so register early.

A company, university, or government email address is required to take part in the hands-on workshop. Personal and disposable email accounts (Gmail, Yahoo, Outlook, and similar) cannot be used for the workshop environment. This is a blanket policy of the workshop platform to prevent fraud and abuse and to meet compliance requirements. If you need an exception, contact the hosts before the event.

Schedule

Welcome and Opening Remarks

Hosts

Agenda

5:30 to 6:00 PM

Doors open, networking, food and drinks

6:00 to 7:30 PM

Workshop

7:30 PM

Networking and close

Workshop Agenda

This hands-on session uses DROID, a real-world robotics dataset loaded into FiftyOne as a native multimodal MCAP recording, and YOLO11n, fine-tuned live during the session.

Welcome + framing: from raw robot logs to a trained detector
Explore a real DROID robotics recording in FiftyOne's native multimodal MCAP viewer: camera, proprioception, and language on one synced timeline, no ROS install required
Curate: extract and browse frames from the recording, filter and deduplicate
Compute embeddings on the curated frames; explore via similarity search and embeddings visualization (via Nebius Serverless AI Jobs)
Auto-label: open-vocabulary detection to generate bounding boxes for the robot gripper and target objects
Train: fine-tune a YOLO11n detector on the auto-labeled frames (via Nebius Serverless AI Jobs)
Evaluate results and close the loop: view predictions back on the original MCAP timeline
What else Nebius offers: Token Factory walkthrough — chat/vision models, fine-tuning, credits