NeuS 2025

Program

Note: Paper titles below link to pre-print versions of the papers. Final versions will be published after the conference.

Wednesday, May 28

Thursday, May 29

Friday, May 30

Keynote 1: Claire Tomlin

Title: Safe Learning for Robotics

Abstract: Automating safety-critical systems demands reliable, understandable control systems. Recent advances in neural networks for control present an exciting future if we can make guarantees about how the control behaves. In this talk, I will discuss three promising directions for neural networks in safety critical control: (1) using neural network to compute safety certificates, (2) certifying learned safety certificates, and (3) interpreting LLM tokens for robotic motion.

Bio: Claire Tomlin is a Professor of Electrical Engineering and Computer Sciences at UC Berkeley, where she holds the James and Katherine Lau Chair in Engineering. Her research interests include hybrid systems, distributed and decentralized optimization, and control theory, with an emphasis on applications, unmanned aerial vehicles, air traffic control and modeling of biological processes. She taught at Stanford University from 1998 to-2007 where she was a director of the Hybrid Systems Laboratory and held joint positions in the Department of Aeronautics and Astronautics and the Department of Electrical Engineering. She was awarded a MacArthur Genius grant in 2006 and the IEEE Transportation Technologies Award in 2017 "for contributions to air transportation systems, focusing on collision avoidance protocol design and avionics safety verification". She is a member of the National Academy of Engineering and the American Academy of Arts and Sciences.

Keynote 2: Sriram Rajamani

Title: Reimagining Large Scale Software Engineering with LLMs

Abstract: Over the past few years LLM-based-tools for code completion have taken the software engineering industry by storm. Tools like GitHub Copilot are used widely by engineers to improve programmer productivity. While LLMs offer significant advantages, several challenges arise with large code bases and large-scale software engineering problems. We believe that several of these challenges can be addressed by combining LLMs with techniques from static program analysis. We describe our work on building tools to solve large scale software engineering problems for very large code bases by combining LLMs together with static analysis methods and point to research opportunities in this area.

Bio: Sriram Rajamani is Corporate Vice President at Microsoft Research. Sriram is also an ACM fellow, INAE fellow, and winner of the Computer Aided Verification Award. His work has impacted both academic and industrial practice in programming languages, systems, security, and formal verification. He is currently working on reimagining the future of programming and software engineering in this era of large AI models. Sriram did his PhD in Computer Science at UC Berkeley.