Welcome to the 2026 IAIFI Summer Workshop!
Jesse Thaler, Former IAIFI Director
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Welcome to the 2026 IAIFI Summer Workshop!
Mike Williams, IAIFI Director
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WiFi Details
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Connect to MIT GUEST.
You will be prompted to provide your email address or mobile phone number for verification.
(Eduroam works as well)
Deep Learning �(AI)
Deep Thinking (Physics)�
Deeper �Understanding
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Generated using Adobe Firefly:
drawing of a brain on a chalkboard with physics equations written inside of it; chalkboard is surrounded by neural networks being injected into the brain; use purple and teal
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A Strategic Approach to AI+Physics
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Pioneering �interdisciplinary
RESEARCH
Building a dynamic
COMMUNITY
Empowering the �next generation of
TALENT
AI for Physics: Developing novel AI to accelerate Physics discovery
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Physics for AI: Leveraging Physics principles to galvanize AI innovation
Tackling two of the greatest mysteries of science through curiosity-driven research: �how our universe works and how intelligence works
A Strategic Approach to AI+Physics
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Pioneering �interdisciplinary
RESEARCH
Building a dynamic
COMMUNITY
Empowering the �next generation of
TALENT
AI for Physics: Developing novel AI to accelerate Physics discovery
+
Physics for AI: Leveraging Physics principles to galvanize AI innovation
Tackling two of the greatest mysteries of science through curiosity-driven research: �how our universe works and how intelligence works
Pioneering Interdisciplinary Research
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Advancing physics knowledge and galvanizing AI research innovation
LHC
IceCube
(& DUNE)
LIGO
Experimental Physics
Enhancing the operations and analysis of flagship NSF experiments through AI
Dark Matter Searches
Astrophysics
Using AI techniques to understand the universe on cosmological scales
Structure Formation
Multi-Messenger Astrophysics
Many-Body Physics
QFT & String Theory
Standard Model
Theoretical Physics
Leveraging AI to understand the theoretical underpinning�of fundamental physics
Representation Learning
Robust/ Interpretable AI
Reinforcement Learning
Foundational AI
Infusing physics principles into AI to create state-of-the-art AI innovations
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AI+Physics Research Impact
KAN: Kolmogorov-Arnold Networks�Ziming Liu, Fabian Ruehle, James Halverson, Marin Soljačić, Max Tegmark�https://arxiv.org/pdf/2404.19756
Over 5k citations
Oral at ICLR 2025
Physics for AI
Sampling QCD field configurations with gauge-equivariant flow models�Michael Albergo, Denis Boyda, Phiala Shanahan, et al
AI for Physics
Mike Williams | 2025 MIT Physics Visiting Committee
FastML Foundation: A non-profit foundation advancing real-time and accelerated ML for fundamental sciences
Phil Harris et al
The DNA of nuclear models: How AI predicts nuclear masses�Kate A. Richardson, Sokratis Trifinopoulos, Mike Williams (based on work with Ziming Liu, Ouail Kitouni, Niklas Nolte, Eric Michaud, and Max Tegmark)�https://arxiv.org/abs/2508.08370
Physics for AI
AI for Physics
Understanding “grokking”
(sudden learning) as a phase transition
Understanding AI �nuclear mass predictions!
AI for Physics
Oral at NeurIPS 2022
Synergistic collaboration with FastML Project
Flagship nuclear theory project
A Strategic Approach to AI+Physics
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Pioneering �interdisciplinary
RESEARCH
Building a dynamic
COMMUNITY
Empowering the �next generation of
TALENT
AI for Physics: Developing novel AI to accelerate Physics discovery
+
Physics for AI: Leveraging Physics principles to galvanize AI innovation
Tackling two of the greatest mysteries of science through curiosity-driven research: �how our universe works and how intelligence works
IAIFI Fellows
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IAIFI Fellow Alumni
AI for String Theory
Thomas Harvey
AI and Statistical Physics
Michael Albergo
AI for Particle Physics
Samuel Bright-Thonney
Physics for AI
Akshunna Dogra
AI for Astroparticle Physics
Siddharth Mishra-Sharma
AI Frontiers of
Reinforcement Learning
Ge Yang
AI for Astrophysics
Alex Gagliano
AI for Particle Physics
Gaia Grosso
AI for Lattice Field Theory
Denis Boyda
AI for Cosmology
Carolina Cuesta-Lazaro
AI for Neutrino Physics
Jessie Micallef
Current & Incoming Fellows
Statistical Foundations of AI
Anna Golubeva
Technical Staff at Zyphra
AI for Quantum
Many-Body Physics
Di Luo
Assoc. Prof at Tsinghua University
Research Scientist at Anthropic &
Asst. Prof at BU
Founder, FortyFive Labs, Inc
Research Scientist at Meta AI
Asst. Prof at NYU
Senior Postdoc for Tufts, based at Fermilab
Applications now being accepted for the sixth round! Deadline: October 7, 2026�https://iaifi.org/fellows
Speaking/Moderating at this year’s Workshop
Asst. Prof at Harvard
Research Scientist at Physical Superintelligence
Senior Postdoc at MIT/Harvard
Physics-based imaging
Berthy Feng
AI for Theoretical Physics
Mathis Gerdes
AI for Gravitational Waves
Christina Reissel
AI for Cosmology
Adrian Bayer
AI for Experimental Particle Physics
Nicole Hartman
AI for Astrophysics
Jacob Nibauer
Education in AI + Physics
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MITx Course (available online!)
Computational Data Science in Physics
Prof. Philip Harris, Prof. Ike Chuang, and Dr. Alex Shvonski
Interdisciplinary PhD
Physics, Statistics, and Data Science�Co-chaired by Jesse Thaler and Mike Williams�
Project 4: �ML modelling of Ising model /Lattice QCD with normalizing Flows
Ouail Kitouni
MIT ⇒ Research Scientist @ Anthropic
“Representations of Physics &
Physics of Representations”
Establishes a verifiable credential that helps Physics students pursue careers in data science and AI
Winner of 2023
MIT’s Teaching
with Digital
Technology Award!
A Strategic Approach to AI+Physics
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Pioneering �interdisciplinary
RESEARCH
Building a dynamic
COMMUNITY
Empowering the �next generation of
TALENT
AI for Physics: Developing novel AI to accelerate Physics discovery
+
Physics for AI: Leveraging Physics principles to galvanize AI innovation
Tackling two of the greatest mysteries of science through curiosity-driven research: �how our universe works and how intelligence works
Public Engagement
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Networking
Local Engagement
Public Colloquia
IAIFI Summer School (just finished!)
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Just completed the fifth IAIFI Summer School!
Happy to welcome many students here, as well as additional researchers joining for the Workshop.
Thank you to the NSF, UMass Boston, Museum of Science, Hudson River Trading, Physical Superintelligence, PDT Partners, Glasswing Ventures, Google DeepMind, Mitsubishi Electric, and FirstPrinciples for their generous financial/in-kind support of the IAIFI Summer School!
IAIFI Summer School
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Lecturers
Tutorial Leads
Miles Cranmer, University of Cambridge�Automatic Equation Discovery
Jun-Yan Zhu, Carnegie Mellon University
Efficient Diffusion Models
Berthy Feng, IAIFI�Computer Vision & Physics
Christoph Weniger, U. of Amsterdam�Dynamic Simulation-Based Inference: Extracting Physics from Complex Data
and Simulations
Jose Munoz, MIT
Automatic Equation Discovery
Mathis Gerdes, IAIFI
Efficient Diffusion Models
Aneel Damaraju, Harvard, and
Franc O., Northeastern�Computer Vision & Physics
Christina Reissel, IAIFI�Dynamic Simulation-Based Inference: Extracting Physics from Complex Data
and Simulations
At the Workshop
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~200 in-person attendees from �~20 different countries
Networking Events (food & beverages provided):
Industry Day
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Join us on Friday, August 14 for our first Industry Day!
All Workshop participants are invited to attend;
Please RSVP for Industry Day and the Workshop dinner so we can plan accordingly for food.
Industry Day - Location
MIT Samberg Conference Center
50 Memorial Dr, Cambridge, MA
Industry Day: Dining Rooms 3&4 (on the 6th floor)
Meal Recommendations
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Pro Tip: Most places will allow you to order in advance online and there are many courtyards around where you could eat outside.
And thank you to the IAIFI Summer Workshop Organizers!
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Will Detmold�MIT
Bill Freeman�MIT
Akshunna Dogra�IAIFI
Berthy Feng�IAIFI
Mathis Gerdes�IAIFI
Juvenal Bassa
UPRM
Yize Dong
Harvard
Franc O
Northeastern
Sneh Pandya
Northeastern
Shelley Tong
MIT
Lana Xu
MIT
Xiaoyuan Zhang
MIT
Marisa LaFleur
IAIFI
Managing Director
Thomas Bradford
IAIFI
Project Coordinator
Leyla Yildiz
IAIFI
Project Administrator
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Before we get started:
Any questions for me?