2026 Conference on Physics and AI (PAI26)

The Center for Decoding the Universe brings together researchers across scientific disciplines to answer the biggest questions about our Universe by leveraging complex data with the most advanced computational methods. 

Bringing together leaders across physics, data science, and AI, Stanford’s Center for Decoding the Universe teams up with the American Physical Society’s Group on Data Science and the NeurIPS Machine Learning and Physical Sciences Workshop team to present the 2026 Conference on Physics and AI (PAI26).

Overview

Advanced statistical and computational insights are transforming all aspects of physics. The goal of Physics and AI (PAI) 2026 is to bring together physics/statistics/data science/computer science researchers, practitioners, and educators who are developing and using AI. Physics here is broadly defined and includes related fields within the physical sciences. The conference will include both work that views physics through the lens of AI as well as research that views AI through the lens of physics. 

Why Physics?

Many areas of physics have been in a big-data, precision era for decades. As such, they were early adopters of AI tools. The deep learning revolution of the last decade has transformative potential in physics, especially given the field’s culture of developing and using advanced statistical and computational techniques. Physics also has unique challenges that necessitate dedicated research and pedagogy. For example, the biggest scientific datasets in existence are from physics experiments, and the data are not always images or text. Additionally, ab initio simulations often play a key role in physics research, which has led to the rise of simulation-based inference. These and other aspects of physics have led to a growing research area at the intersection of physics and AI—with data physicists and phystatistians emerging as researchers at the forefront of novel methodology. 

Conference Structure

This Conference is modeled after the Machine Learning and Physical Sciences Workshop (MLPS) at NeurIPS. We welcome short research papers that will be peer reviewed and then ranked for impact by area chairs. Accepted papers will be published, and authors will be invited to present a poster. A small number of accepted papers will be selected for spotlight talks. The rest of the program will include invited talks and panels. Paper submission is open to all. 

Call for Papers 

The OpenReview page for paper submission and review is now available.

Registration 

To be announced soon!

Organizing Committees

Local Organizers and Advisors

• Tom Abel
• Susan Clark
• Surya Ganguli
• Sanmi Koyejo
• Phil Marshall
• Benjamin Nachman
• Chris Re
• Risa Wechsler

Non-local Organizers and Advisors (from GDS and MLPS)

• Nina Andrejevic, Argonne National Lab.
• Casey Berger, Bates College
• Jerome Delhommelle, U. Mass Lowell
• Ivo Dinov, U. Michigan
• Cristiano Fanelli, William & Mary
• Eun-Ah Kim, Cornell University
• Garrett Merz, U. Wisconsin
• Vinicius Mikuni, U. Nagoya
• Siddharth Mishra-Sharma, BU/Anthropic
• Mark Neubauer, UIUC
• Ryotaro Okabe, MIT
• Entao Yang, Air Liquide