IC Spring Seminar Series with Nicholas Sharp

Abstract

The world around us is inherently 3D, and this structure is essential from engineering and the physical sciences to photogrammetry and robotics. Yet computing and learning with real-world geometric data remains remarkably difficult, stubbornly stuck on challenges of robustness, representation, and scale. Noisy scans, mismatched representations, and massive datasets make even basic operations fragile and slow, while learning with 3D data demands a new set of architectures purpose-built for the task.

This talk will present methods that unlock 3D data for reliable processing and learning in the wild. I'll cover neural network architectures designed specifically for 3D data to offer physical consistency and geometric fidelity by-construction, simulation techniques that operate natively on scanned environments, and the foundational geometric algorithms that underpin it all. Throughout, a key theme is interfacing principled geometric methods with modern machine learning.

Bio

Nicholas Sharp's research develops algorithms and architectures to make computing with geometric data easy, efficient, and reliable. He received his PhD in Computer Science from Carnegie Mellon University advised by Keenan Crane, and completed his postdoc at the University of Toronto & Fields Institute for Mathematics with Alec Jacobson. Currently, he is a Senior Research Scientist at NVIDIA, leading research on 3D learning that powers applications from autonomous driving to generative AI. His work has won multiple best paper awards (SIGGRAPH '22, SIGGRAPH Asia '23, SGP '20 & '25), developed widely-used software including the award-winning Polyscope library, and has been covered in the press by the New York Times and Ars Technica. For more, see www.nmwsharp.com.