AI Professionals NYC Machine Learning

Sep 17, 2026
9:00 AM - 11:00 AM PST
Online. Register for the Zoom!

AI for Autonomous Driving: From Data to Decisions

Building reliable automated driving systems is as much a data and engineering challenge as a modeling one. In this talk, Tin will share perspectives from his work at Porsche AG on applying modern AI methods across the autonomous driving development process, from making sense of large-scale driving data to understanding and evaluating how AI-based systems behave on the road. He'll discuss lessons learned from real-world development, where today's approaches shine, and where hard problems remain for the ADAS and AV community.

About the Speaker

Tin Stribor Sohn is a PhD Student at Porsche AG and Karlsruhe Institute of Technology in the area of Foundation Models for Scenario Understanding and Decision Making in Autonomous Robotics, Tech Lead at Data Driven Engineering for Autonomous Driving, Prior: Master in CS at University of Tuebingen with focus on Computer Vision and Deep Learning and co-founder of a software company for smart EV charging

Advancing ADAS and Autonomous Vehicle Development with Multimodal Data

ADAS and autonomous vehicle systems rely on increasingly complex data from cameras, video, LiDAR, radar, and other sensor streams. In this session, Murilo will introduce Voxel51 and explore how the latest multimodal capabilities in FiftyOne help teams bring these data sources together to better understand their datasets and model behavior. He’ll discuss how unified workflows for visualization, search, curation, and evaluation can help ADAS and AV teams uncover challenging scenarios, investigate model failures, and build safer, more reliable autonomous systems.

About the Speaker

Murilo Gustineli is a Machine Learning Engineer at Voxel51 working at the intersection of representation learning and computer vision. He holds an M.S. in Computer Science from Georgia Tech, where he co-leads the DS@GT Applied Research & Competitions group, advancing machine learning research through competitive challenges and peer-reviewed publications.

From Survey-Grade Maps to Physical AI: Scaling Real-World Data for Training and Simulation

Physical AI systems are increasingly constrained not by model architectures, but by the availability of scalable, high-fidelity real-world data. This talk explores how Dynamic Map Platform transforms survey-grade road assets collected across 1.8 million km of roads worldwide into training- and simulation-ready datasets, including point clouds, imagery, HD maps, road surface models, and 3D Gaussian Splatting representations.

We will discuss why geometric accuracy, semantic understanding, and real-world diversity are critical to building robust autonomous driving systems. Attendees will learn how real-world geospatial data can be structured and scaled for AI training and simulation workflows.

About the Speaker

Ryoto Miyake is a Software Engineer at Dynamic Map Platform, where he works on transforming large-scale geospatial data into AI-ready datasets for training, simulation, and validation, such as HD maps and 3D Gaussian Splatting. With a background in transportation engineering, he works closely with automotive manufacturers and industry partners to bridge large-scale real-world mapping data with next-generation AI and mobility systems.

Seats are limited, Pre-registration is mandatory.

Date, Time and Location

Nov 14, 2026
1:30 PM - 4:30 PM
NYU Kimmel Center - Room 914
60 Washington Square South
New York, NY 10012

Meetup Agenda
5:30–6:30 PM

• Networking, food, drinks, and lightning talks

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.

6:30–7:30 PM

• 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)

7:30–8:00 PM

• 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

8:00–8:30 PM

• What else Nebius offers: Token Factory walkthrough — chat/vision models, fine-tuning, credits