By combining revolutionary advances in deep learning from AI with the time-tested strategies of deep thinking from physics, IAIFI researchers are gaining a deeper understanding of our universe and of intelligence itself. IAIFI’s efforts have helped to establish the interdisciplinary field of AI+Physics, combining AI innovation with inductive biases from physics to advance both fields in a virtuous cycle. IAIFI is facilitating collaborations across the domains of Foundational AI, Theoretical Physics, Experimental Physics, and Astrophysics by using cross-cutting themes of Representation/Manifold Learning, Generative Models, Uncertainty Quantification/Robust AI, Physics-Motivated Optimization, and Reinforcement Learning to develop a common language.
Domain Impact
Highlights
Denoising Hamiltonian Network for Physical Reasoning
Congyue Deng (MIT/Stanford, Incoming IAIFI Junior Investigator), Brandon Y. Feng (MIT/IAIFI), Cecilia Garraffo (Harvard/IAIFI), Alan Garbarz (Universidad de Buenos Aires and Instituto de Física de Buenos Aires), Robin Walters (Northeastern/IAIFI), William T. Freeman (MIT/IAIFI), Leonidas Guibas (Stanford), Kaiming He (MIT/IAIFI)
Generative Modeling for Mathematical Discovery
Jordan S. Ellenberg (University of Wisconsin-Madison), Cristofero S. Fraser-Taliente (University of Oxford), Thomas R. Harvey (IAIFI Fellow), Karan Srivastava (University of Wisconsin-Madison), Andrew V. Sutherland (MIT)
A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing
Julia Balla (MIT/IAIFI), Siddharth Mishra-Sharma (Former IAIFI Fellow), Carolina Cuesta-Lazaro (IAIFI Fellow), Tommi Jaakkola (MIT/IAIFI), Tess Smidt (MIT/IAIFI)
Reinforcement Learning for Optimal Control of Adaptive Cell Populations
Josiah C. Kratz (Carnegie Mellon University), Jacob Adamczyk (UMass Boston/IAIFI)
Towards Universal Unfolding of Detector Effects in High-Energy Physics using Denoising Diffusion Probabilistic Models
Camila Pazos (Tufts/IAIFI), Shuchin Aeron (Tufts/IAIFI), Pierre-Hugues Beauchemin (Tufts/IAIFI), Vincent Croft, Zhengyan Huan (Tufts), Martin Klassen, Taritree Wongjirad (Tufts/IAIFI)
Maven: A Multimodal Foundation Model for Supernova Science
Alexander T. Gagliano, Siddharth Mishra-Sharma (IAIFI Fellows), V. Ashley Villar, Gemma Zhang (Harvard), Thomas Helfer (Stony Brook)
QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation
Zhuo Chen, Rumen Dangovski, Charlotte Loh, Owen Dugan, Di Luo, Marin Soljačić (MIT/IAIFI)
Learning the Simplicity of Scattering Amplitudes
Matthew D. Schwartz, Aurelian Dersy (Harvard/IAIFI) and Clifford Cheung (Caltech)
From Neurons to Neutrons: A Case Study in Interpretability
Ouail Kitouni (MIT/IAIFI), Niklas Nolte (MIT/IAIFI), Vı́ctor Samuel Pérez-Dı́az (Harvard/IAIFI), Sokratis Trifinopoulos (MIT/IAIFI), Mike Williams (MIT/IAIFI)
Anomaly-aware summary statistic from data batches
Gaia Grosso (IAIFI Fellow)
KAN: Kolmogorov-Arnold Networks
IAIFI: Ziming Liu, Fabian Ruehle, James Halverson, Marin Soljačić, Max Tegmark; Collaborators: Yixuan Wang, Sachin Vaidya, Thomas Y. Hou
Using Machine Learning for Neutrino Detection
IceCube Collaboration (Jessie Micallef, IAIFI Fellow)
Model-agnostic search for dijet resonances with anomalous jet substructure in proton-proton collisions at √s = 13TeV
Phil Harris, Patrick McCormack, Sang Eon Park (MIT), Sam Bright-Thonney (incoming Fellow), part of CERN CMS collaboration
Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation
Ameya Daigavane, Song Eun Kim, Mario Geiger, Tess Smidt (MIT)
A point cloud approach to generative modeling for galaxy surveys at the field level
Carolina Cuesta-Lazaro, Siddharth Mishra-Sharma (IAIFI Fellows)
Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation
Ge Yang (IAIFI Fellow), William Shen, Alan Yu, Jansen Wong, Leslie Pack Kaelbling, Phillip Isola (MIT)
Cosmological Data Compression and Inference with Self-Supervised Machine Learning
Aizhan Akhmetzhanova (Harvard), Siddharth Mishra-Sharma (IAIFI Fellow), Cora Dvorkin (Harvard)
Improving the performance of the Laser Interferometer Gravitational-wave Observatory (LIGO) with AI
Lisa Barsotti, Nikhil Mukund, Chris Whittle, Matt Evans, Pulkit Agrawal, (MIT) Ge Yang (IAIFI Fellow)
Dynamic Sparse Training
Mike Lasby, Anna Golubeva, Utku Evci, Mihai Nica, Yani A. Ioannou
Learning Athletic, Context-Adaptive Robot Locomotion
Pulkit Agrawal, Gabriel B Margolis, Yandong Ji (MIT)
Uncovering dark matter density profiles in dwarf galaxies with graph neural networks
Tri Nguyen (MIT), Siddharth Mishra-Sharma (IAIFI Fellow), Lina Necib (MIT)
SHAPER: Can You Hear the Shape of a Jet?
Demba Ba (Harvard, IAIFI), Akshunna S. Dogra (Imperial College London, Harvard, IAIFI), Rikab Gambhir (MIT, IAIFI), Abiy Tasissa (Tufts), Jesse Thaler (MIT, IAIFI)
Infinite Neural Network Quantum States
Di Luo (IAIFI Fellow), James Halverson (Northeastern)
An Effective Theory of Representation Learning
Ziming Liu, Ouail Kitouni, Niklas Nolte, Eric J. Michaud, Max Tegmark, Mike Williams (MIT, IAIFI)
Normalizing Flows for Lattice Quantum Field Theory
Phiala Shanahan, Dan Hackett, Fernando Romero-Lopez, Julian Urban (MIT/IAIFI), Denis Boyda (incoming IAIFI fellow), Ryan Abbott (MIT), Kyle Cranmer (NYU), Michael Albergo (NYU), Danilo Rezende (Google DeepMind), Sebastien Racaniere (Google DeepMind)
Strong Lensing Source Reconstruction
Siddharth Mishra-Sharma, Ge Yang (IAIFI Fellows)
Robust AI for Real-Time Applications
Ouail Kitouni, Niklas Nolte, Blaise Delaney, Mike Williams (MIT, IAIFI)