Dec 9 - Ann Arbor AI, ML and Computer Vision Meetup

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

Date, Time and Location

Dec 9, 2025
5:30 - 8:30 PM

Preregister to reserve your spot

Cahoots Coworking Space
206 E Huron St,
Ann Arbor, MI 48104

Stay tuned! More speakers to be announced shortly.

Challenges and Opportunities for AI in Marine Robotics

Underwater robots are essential tools for ocean exploration. This presentation will explore new technology in robotics and artificial intelligence that can enable automated detection of shipwreck sites from sonar data collected from autonomous underwater vehicles (AUVs). We will also discuss our recent work on underwater mapping and 3D reconstruction towards real-time dense mapping of shipwreck sites from sonar and camera imagery. Results will be demonstrated on data from field trials in Thunder Bay National Marine Sanctuary in Lake Huron.

About the Speaker

Katie Skinner is an Assistant Professor in the Department of Robotics at the University of Michigan. She received an M.S. and Ph.D. from the Robotics Institute at the University of Michigan, and a B.S.E. in Mechanical and Aerospace Engineering with a Certificate in Applications of Computing from Princeton University. She is a recipient of the NSF CAREER Award, ONR Young Investigator Award, and IEEE Robotics and Automation Letters Best Paper Award.

Building Smarter AV Simulation with Neural Reconstruction and World Models

This talk explores how neural reconstruction and world models are coming together to create richer, more dynamic simulation for scalable autonomous vehicle development. We’ll look at the latest releases in 3D Gaussian splatting techniques and world reasoning and generation, as well as discuss how these technologies are advancing the deployment of autonomous driving stacks that can generalize to any environment. We’ll also cover NVIDIA open models, frameworks, and data to help kickstart your own development pipelines.

About the Speaker

Katie Washabaugh is NVIDIA’s Product Marketing Manager for Autonomous Vehicle Simulation, focusing on virtual solutions for real world mobility. A former journalist at publications such as Automotive News and MarketWatch, she joined the NVIDIA team in 2018 as Automotive Content Marketing Manager. Katie holds a B.A. in public policy from the University of Michigan and lives in Detroit.

In-Cabin Intelligence: AI's Vision for Predictive Cabin Safety

Current in-cabin monitoring systems react to distractions and drowsiness after they occur. Cabin AI: Predictive Safety introduces a paradigm shift using multi-modal sensor fusion and deep learning to anticipate dangerous states before they manifest. By analyzing driver gaze, occupant behavior, and physiological signals, these systems predict impairment, distraction, and risky activities with over 95% accuracy, meeting stringent Euro NCAP 2025 requirements. This proactive approach not only prevents accidents but builds passenger trust in autonomous vehicles. Discover how predictive algorithms are transforming cabin sensing from a compliance checkbox into a core safety differentiator.

About the Speaker

Vijayachandar Sanikal, IEEE Senior Member, is a visionary automotive leader with 20+ years pioneering AI-driven thermal optimization and cloud-based simulation for electric and autonomous vehicles. He builds intelligent mobility solutions at the intersection of digital twins, software-defined vehicles, and sustainable innovation.

From Raw Sensor Data to Reliable Datasets: Physical AI in Practice

Modern mobility systems rely on massive, high-quality multimodal datasets — yet real-world data is messy. Misaligned sensors, inconsistent metadata, and uneven scenario coverage can slow development and lead to costly model failures. The Physical AI Workbench, built in collaboration between Voxel51 and NVIDIA, provides an automated and scalable pipeline for auditing, reconstructing, and enriching autonomous driving datasets.

In this talk, we’ll show how FiftyOne serves as the central interface for inspecting and validating sensor alignment, scene structure, and scenario diversity, while NVIDIA Neural Reconstruction (NuRec) enables physics-aware reconstruction directly from real-world captures. We’ll highlight how these capabilities support automated dataset quality checks, reduce manual review overhead, and streamline the creation of richer datasets for model training and evaluation.

Attendees will gain insight into how Physical AI workflows help mobility teams scale, improve dataset reliability, and accelerate iteration from data capture to model deployment — without rewriting their infrastructure.

About the Speaker

Daniel Gural leads technical partnerships at Voxel51, where he’s building the Physical AI Workbench, a platform that connects real-world sensor data with realistic simulation to help engineers better understand, validate, and improve their perception systems. With a background in developer relations and computer vision engineering,