Research

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)

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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)

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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)

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Reinforcement Learning for Optimal Control of Adaptive Cell Populations

Josiah C. Kratz (Carnegie Mellon University), Jacob Adamczyk (UMass Boston/IAIFI)

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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)

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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)

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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)

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Learning the Simplicity of Scattering Amplitudes

Matthew D. Schwartz, Aurelian Dersy (Harvard/IAIFI) and Clifford Cheung (Caltech)

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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)

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Anomaly-aware summary statistic from data batches

Gaia Grosso (IAIFI Fellow)

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KAN: Kolmogorov-Arnold Networks

IAIFI: Ziming Liu, Fabian Ruehle, James Halverson, Marin Soljačić, Max Tegmark; Collaborators: Yixuan Wang, Sachin Vaidya, Thomas Y. Hou

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Using Machine Learning for Neutrino Detection

IceCube Collaboration (Jessie Micallef, IAIFI Fellow)

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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

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Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation

Ameya Daigavane, Song Eun Kim, Mario Geiger, Tess Smidt (MIT)

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A point cloud approach to generative modeling for galaxy surveys at the field level

Carolina Cuesta-Lazaro, Siddharth Mishra-Sharma (IAIFI Fellows)

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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)

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Cosmological Data Compression and Inference with Self-Supervised Machine Learning

Aizhan Akhmetzhanova (Harvard), Siddharth Mishra-Sharma (IAIFI Fellow), Cora Dvorkin (Harvard)

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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)

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Dynamic Sparse Training

Mike Lasby, Anna Golubeva, Utku Evci, Mihai Nica, Yani A. Ioannou

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Learning Athletic, Context-Adaptive Robot Locomotion

Pulkit Agrawal, Gabriel B Margolis, Yandong Ji (MIT)

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Uncovering dark matter density profiles in dwarf galaxies with graph neural networks

Tri Nguyen (MIT), Siddharth Mishra-Sharma (IAIFI Fellow), Lina Necib (MIT)

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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)

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Infinite Neural Network Quantum States

Di Luo (IAIFI Fellow), James Halverson (Northeastern)

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An Effective Theory of Representation Learning

Ziming Liu, Ouail Kitouni, Niklas Nolte, Eric J. Michaud, Max Tegmark, Mike Williams (MIT, IAIFI)

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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)

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Strong Lensing Source Reconstruction

Siddharth Mishra-Sharma, Ge Yang (IAIFI Fellows)

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Robust AI for Real-Time Applications

Ouail Kitouni, Niklas Nolte, Blaise Delaney, Mike Williams (MIT, IAIFI)

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