AI3 Seminar
Monday, October 26, 2026
12:30 PM
New Computer Science Bldg 120
Registration is required
From Particle-Collision Foundation Models to LLM-Guided Analog Circuit Optimization
Yihui "Ray" Ren
AI Department, Brookhaven National Laboratory
Abstract: Artificial intelligence is rapidly changing how we approach scientific discovery and engineering design. However, realizing the potential of modern AI in scientific domains requires going beyond simply applying models developed for natural language: scientific data have distinct structures, and scientific workflows often require tight integration among learned representations, domain knowledge, simulation, and numerical optimization.
In this talk, I will present two recent efforts that explore complementary directions for building AI systems for science and engineering. First, FM4NPP investigates whether the foundation-model paradigm can scale to experimental nuclear and particle physics. Using more than 11 million particle-collision events, we develop a self-supervised learning framework for sparse detector data and demonstrate scaling up to 188 million parameters. The resulting representations generalize across diverse downstream tasks and enable data-efficient adaptation with lightweight task-specific components.
Second, I will present AutoSizer, an LLM-driven framework for automatic sizing of analog and mixed-signal circuits. Rather than asking an LLM to perform numerical optimization directly, AutoSizer uses the LLM as a reasoning and orchestration layer that interprets circuit behavior, analyzes simulation feedback, and adaptively refines the optimization process.
Together, these studies illustrate two complementary roles for modern AI in scientific workflows: learning reusable representations from complex scientific data and reasoning over computational tools to guide challenging design and optimization problems. I will discuss the lessons from these efforts and opportunities toward more general, scalable, and reusable AI systems for science and engineering.
Short bio: Dr. Yihui (Ray) Ren is a Senior Computational Scientist in AI/ML and the AI-Codesign Group Lead in the Artificial Intelligence Department within Brookhaven National Laboratory’s Computing and Data Sciences Directorate. His research focuses on AI for Science, AI–hardware codesign, real-time AI systems, and the evaluation of emerging AI hardware architectures. He also develops advanced AI methodologies for nuclear safeguards applications. Dr. Ren’s key contributions include unpaired domain mapping techniques to mitigate domain shift, generative surrogate modeling, object detection and video synthesis using event-based cameras, and the development of AI foundation models such as the Foundation Model for Nuclear and Particle Physics (FM4NPP). Prior to joining Brookhaven National Laboratory in 2018, he was a postdoctoral researcher at Virginia Tech. He received his Ph.D. in Physics from the University of Notre Dame in 2015.