IAIFI + CSAIL Joint Colloquium - Friday 11/21/2025
Thomas Bradford
Hi all,
Next Friday (November 21, 2025), the NSF Institute for Artificial Intelligence and Fundamental Interactions (IAIFI) and MIT CSAIL will co-host a public colloquium. The details are below. We hope you can join us!
Best,
Thomas
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Details:
2:00pm ET Friday, November 21, 2025
IAIFI and CSAIL Public Colloquium (https://iaifi.org/events.html)
Symbiosis of Physics and Artificial Intelligence
T. Konstantin Rusch, Assistant Professor, Max Planck Institute for Intelligent Systems
Watch on YouTube: https://www.youtube.com/channel/UCueoFcGm_15kSB-wDd4CBZA
Abstract: Artificial Intelligence (AI) is transforming how we advance science and engineering, yet current approaches remain limited by computational inefficiencies, insufficient theoretical grounding, and a focus on commercial rather than scientific applications. To address these, we propose an interdisciplinary research agenda that bridges physics and AI along two complementary directions: physics for AI and AI for physics. In the former, we develop physics-inspired AI models that exploit structural principles from physical systems to design architectures that are more efficient, expressive, and mathematically tractable. We further enhance scalability of our proposed models through control-theoretic in-training compression methods that enable efficient training of large models. In the latter direction, we apply AI, including our physics-inspired architectures, to challenging problems in the physical sciences, such as gravitational-wave analysis and scientific computing. One highlighted contribution in this context is Message-Passing Monte Carlo (MPMC), the first machine-learning approach for generating low-discrepancy point sets, which combines geometric deep learning with discrepancy theory and achieves 4- to 25-fold performance improvements over prior methods in physics, scientific machine learning, and robotics applications. Together, these efforts highlight the potential of uniting physics and AI to achieve models that are both powerful and principled, paving the way for the next generation of scientific AI systems.
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Thomas Bradford
Project Coordinator, IAIFI