The IAIFI Summer Workshop brings together researchers from across Physics and AI for plenary talks, poster sessions, and networking to promote research at the intersection of Physics and AI. We are also accepting submissions for contributed talks and/or posters.

• The 2025 Summer Workshop will be held August 11–15, 2025
• Location: Harvard University (Cambridge, MA)
• Registration is now open for the 2025 IAIFI Summer Workshop; Register by July 31, 2025 ($200 registration fee).

Register for the 2025 IAIFI Summer Workshop

Here’s what attendees at previous IAIFI Summer Workshops had to say about the experience:

Agenda Speakers FAQ Accommodations Past Workshops

About

The Institute for Artificial Intelligence and Fundamental Interactions (IAIFI) is enabling physics discoveries and advancing foundational AI through the development of novel AI approaches that incorporate first principles, best practices, and domain knowledge from fundamental physics. The goal of the Workshop is to serve as a meeting place to facilitate advances and connections across this growing interdisciplinary field.

Agenda

The agenda is subject to change.

Monday, August 11, 2025

9:00-9:15 am ET

Welcome

9:15–10:00 am ET

AI in Astrophysics: Tackling Domain Shift, Model Robustness and Uncertainty

Aleksandra Ciprijanovic, Fermilab

Abstract

Artificial Intelligence is transforming astrophysics, from studying stars and galaxies to analyzing cosmic large-scale structures. However, a critical challenge arises when AI models trained on simulations or past observational data are applied to new observation— leading to domain shifts, reduced robustness, and increased uncertainty of model predictions. This talk will explore these issues, highlighting examples such as galaxy morphology classification and cosmological parameter inference, where AI struggles to adapt across different datasets. We will discuss domain adaptation as a strategy to improve model generalization and mitigate biases—essential for making AI-driven discoveries reliable. Notably, these challenges extend beyond astrophysics, affecting AI applications across physics and other scientific domains. Addressing them is essential for maximizing AI’s impact in advancing scientific research.

10:00–10:45 am ET

Theoretical foundations for language model self-improvement

Dylan Foster, Microsoft Research

Abstract

Abstract to come.

10:45-11:15 am ET

Break

11:15 am–12:00 pm ET

Self-supervised Reinforcement Learning and Generative Models

Amy Zhang, UT Austin

Abstract

We explore the intersection of generative AI and reinforcement learning, demonstrating how generative models can be understood as RL agents and environments, and conversely, how RL can be viewed as generative modeling. We show how this perspective leads to new forms of self-supervised reinforcement learning algorithms that form new objectives for training generative models. We will discuss future directions and open problems, focusing on how RL can shape the future of foundation model training.

12:00–1:30 pm ET

Lunch Break

1:30–2:15 pm ET

Machine Learning for Time-Domain Astrophysics

Alex Gagliano, IAIFI Fellow

Abstract

Abstract to come.

2:15–3:00 pm ET

Emulating Dark Matter Halo Merger Trees with Generative Models

Tri Nguyen, CIERA, Northwestern University

Abstract

Merger trees track the hierarchical assembly of dark matter halos and are crucial for semi-analytic galaxy formation models. However, traditional methods rely on ad-hoc assumptions and struggle to incorporate environmental information. I present FLORAH-Tree, a generative model for merger trees by representing them as graph structures that capture the full branching hierarchy. I trained FLORAH-Tree on merger trees from the Very Small MultiDark Planck N-body simulation and validated it against simulation data and Extended Press-Schechter analytical trees. FLORAH-Tree accurately reproduces key merger tree statistics across mass and redshift ranges, outperforming analytical approaches. Applying the Santa Cruz semi-analytic model to generated trees shows that galaxy-halo scaling relations match expectations. FLORAH-Tree provides a computationally efficient method for generating merger trees while maintaining N-body simulation fidelity.

3:00–3:30 pm ET

Break

3:30–4:15 pm ET

Multimodal Foundation Models for Scientific Data

Francois Lanusse, CNRS

Abstract

Deep Learning has seen a recent shift in paradigm, from training specialized models on dedicated datasets, so-called Foundation Models, trained in a self-supervised manner on vast amounts of data and then adapted to solve specific tasks with state-of-the-art performance. This new paradigm has been exceptionally successful not only for large language models (LLMs) but in other domains such as vision models. In this talk I will discuss several methodologies that can be deployed in our scientific domains, in particular weakly-supervised cross-modal contrastive learning and multimodal self-supervised generative pretraining. I will show how these approaches can be used to build models flexible to very diverse and inhomogeneous observations (e.g. different types of measurements such as spectra, time-series, or images, but also different instruments, etc...) and how they can then be leveraged by the end-user for a variety of downstream applications (e.g. redshift estimation, morphology classification, physical parameter inference, searching for rare objects) with very simple machine learning methods and still reach near-optimal performance.

4:15-5:00 pm ET

Structured Learning for Astrophysical Data

Peter Melchior, Princeton University

Abstract

Astronomy and astrophysics are currently undergoing profound changes as the results of two developments: 1) the availability of vast quantities of data from surveys and simulations, and 2) the rapid progress in machine learning and AI. But how should we bridge the gap between data-driven and theoretical descriptions of the Universe? How do we actually learn new aspects of physical systems from data? I will show that introducing structure, usual reflecting causal relations, into deep learning architectures creates efficient, robust, useful, and highly interpretable models that respect known physics and reveal unknown phenomena. I will present results from my group on exoplanets, galaxy evolution, and cosmology to demonstrate what we can achieve already and discuss how this approach leads to future astrophysics and AI research.

5:30–8:00 pm ET

Poster Session and Reception

Tuesday, August 12, 2025

9:00–9:45 am ET

(Machine) Learning of Dark Matter

Lina Necib, MIT

Abstract

Abstract to come.

9:45–10:30 am ET

Learning the Universe: Building a Scalable, Verifiable Emulation Pipeline for Astronomical Survey Science

Matthew Ho, Columbia University

Abstract

Learning the Universe is developing a large-scale, ML-accelerated pipeline for simulation-based inference in cosmology and astrophysics. By combining high-resolution physical models with fast emulators, we generate realistic training sets at the scale required for field-level inference from galaxy survey data. This enables us to constrain models of galaxy formation and cosmology from observations with unprecedented scale and precision. In designing this pipeline, we have also developed validation methodologies to assess emulator accuracy, identify sources of systematic error, and support blinded survey analysis. I will present results from its application to the SDSS BOSS CMASS spectroscopic galaxy sample and discuss how this approach is scaling to upcoming cosmological surveys.

10:30-11:00 am ET

Break

11:00–11:45 am ET

High-dimensional Bayesian Inference with Diffusion Models and Generative Flow Networks

Alexandre Adam, Université de Montréal

Abstract

The nature of dark matter is one of the greatest mystery in modern cosmology. Little is known about dark matter, other than it has a mass, thus gravitate, and interact with the electromagnetic field weakly, if none at all. Gravitational lensing is a natural phenomena which involves the trajectory of photons from distant galaxies bending by the gravity of massive object in our line of sight. As such, it is one of the most promising probe to study the nature of dark matter. This talk will discuss the problem of inferring the mass of the hypothetic dark matter particle from strong gravitational lens measurement, the challenges involved in such an inference from a Bayesien perspective and the potential solutions offered by modern deep learning framework such as diffusion models and generative flow networks.

11:45 am–12:30 pm ET

Bridging Legacy and Modern Inference: Practical SBI for Astrophysics

Noemi Anau Montel, Max Planck Institute for Astrophysics

Abstract

Simulation-based inference (SBI) has emerged as a transformative tool for astrophysical analysis, yet its widespread adoption still faces significant obstacles, including the integration with existing pipelines and establishing scientific trust. This talk addresses these challenges through three practical approaches demonstrated across different applications. In particular, I will discuss how to build effective simulators from explicit likelihood codes, allowing us to integrate legacy cosmological likelihoods, like the Planck CMB likelihood, with SBI frameworks. Second, I will present a robust SBI pipeline for complex Fermi-LAT gamma-ray observations that, while modeling background emission with greater realism, confirms source-count distributions consistent with traditional results and achieves $>98\%$ completeness relative to the standard catalog. Finally, I will introduce systematic tests for model misspecification by extending traditional goodness-of-fit concepts to simulation-based frameworks, showing an example of such tests on the GW150914 gravitational wave event.

12:30–2:00 pm ET

Lunch

2:00–3:30 pm ET

Contributed Talks - Parallel Sessions

3:30–4:00 pm ET

Break

4:00–4:45 pm ET

Title to come

Hidenori Tanaka, Harvard University/NTT Research, Inc.

Abstract

Abstract to come.

4:45-5:30 pm ET

Computing with Neural Manifolds in Biological and Artificial Neural Networks

SueYeon Chung, Harvard University (starting Fall 2025), Flatiron Institute

Abstract

Abstract to come

Wednesday, August 13, 2025

9:00–9:45 am ET

From Neurons to Neutrons: An Intepretable AI model for Nuclear Physics

Sokratis Trifinopoulos, MIT/CERN

Abstract

Abstract to come.

9:45–10:30 am ET

Investigating Proton Spatial and Spin Structure with Interpretable AI

Simonetta Liuti, University of Virginia

Abstract

Abstract to come.

10:30-11:00 am ET

Break

11:00–11:45 am ET

Title to come

Lukas Heinrich, Technical University Munich

Abstract

Abstract to come.

11:45 am–12:30 pm ET

AI on the Edge: Decoding Particles, Brains, and Cosmic Collisions in Real Time

Shih-Chieh Hsu, University of Washington

Abstract

Artificial Intelligence is transforming scientific discovery at every scale-from the subatomic to the cosmic-by enabling real-time data analysis with unprecedented speed and precision. The A3D3 Institute leads this revolution, leveraging advanced hardware like FPGAs and GPUs, cutting-edge model compression, and specialized inference frameworks to accelerate breakthroughs in particle physics, neuroscience, and multi-messenger astrophysics. This talk highlights how A3D3’s innovations are powering instant detection of rare events, live decoding of brain signals, and rapid response to cosmic phenomena, ushering in a new era where AI turns massive data streams into actionable insights as they happen.

12:30–2:00 pm ET

Lunch

2:00–3:30 pm ET

Contributed Talks - Parallel Sessions

3:30–4:00 pm ET

Break

4:00–5:00 pm ET

Building an AI Scientist: Best Practices for Vibe Coding

Panel TBA

6:00–8:00 pm ET

Workshop Dinner

Museum of Science

Thursday, August 14, 2025

9:00–9:45 am ET

Low latency machine learning at the LHCb experiment

Eluned Smith, MIT

Abstract

With an anticipated input rate of 200 Tb/s, the LHCb experiment demands real-time data reduction at the detector level to prevent irreversible loss of valuable physics information. This talk presents an overview of efforts to deploy machine learning inference directly on compact, radiation-tolerant FPGAs located near the detector readout—an environment that imposes stringent constraints on latency and resource utilization. As a concrete example, I highlight the use of Variational Autoencoders (VAEs) that can simultaneously compress waveforms and extract precise timestamps within the required latency budget. I also discuss other potential and ongoing machine learning applications within LHCb's front-end systems.

9:45–10:30 am ET

Towards Complete Automation in Particle Image Inference

Francois Drielsma, SLAC

Abstract

Particle imaging detectors have had a ubiquitous role in particle physics for over a century. The unrivaled level of detail they deliver has led to many discoveries and continues to make them an attractive choice in modern experiments. The liquid argon time projection chamber (LArTPC) technology – a dense, scalable realization of this detection paradigm – is the cornerstone of the US-based accelerator neutrino program. While the human brain can reliably recognize patterns in particle interaction images, automating this reconstruction process has been an ongoing challenge which could jeopardize the success of LArTPC experiments. Recent leaps in computer vision, made possible by machine learning (ML), have led to a remedy. We introduce an ML-based data reconstruction chain for particle imaging detectors: a multi-task network cascade which combines voxel-level feature extraction using Sparse Convolutional Neural Networks and particle superstructure formation using Graph Neural Networks. It provides a detailed description of an image and is currently used for state-of-the-art physics inference in three LArTPC experiments. Building on this success, we briefly inrtoduce the potential of leveraging self-supervised learning – the core concept of cutting-edge large language models – to learn the fundamental structure of detector data directly from a large corpus of raw, unlabeled data. This novel approach could address current shortcomings in signal processing and reduce the impact of data/simulation disagreements.

10:30-11:00 am ET

Break

11:00–11:45 am ET

Foundation Models for Detector Data: progress, potential, and concerns

Michelle Kuchera, Davidson College

Abstract

Abstract to come.

11:45 am–12:30 pm ET

Deep(er) Reconstruction of Imaging Cherenkov Detectors: From Generative Towards Foundation Models

Cristiano Fanelli, William & Mary

Abstract

Abstract to come.

12:30–2:00 pm ET

Lunch

2:00–3:30 pm ET

Contributed Talks - Parallel Sessions

3:30–4:00 pm ET

Break

4:00–4:45 pm ET

Machine Learning inroads into the Bootstrap programme

Costis Papageorgakis, Queen Mary University of London

Abstract

I will survey recent advances in applying machine learning to the bootstrap programme in theoretical physics. I will discuss the use of reinforcement learning in high-dimensional searches for Conformal Field Theory (CFT) data, neural operators for finding scattering-matrix phases and neural network applications for bootsrapping CFTs at finite temperature. I will also touch upon investigations using large language models for analysing high-energy theory abstracts.

4:45–5:30 pm ET

Title to come.

Sven Krippendorf, University of Cambridge

Abstract

Abstract to come.

Friday, August 15, 2025

9:00–9:45 am ET

Generative quantum advantage for classical and quantum problems

Hsin-Yuan (Robert) Huang, Caltech, Google

Abstract

Abstract to come.

9:45–10:30 am ET

Artificial intelligence for quantum matter

Liang Fu, MIT

Abstract

Abstract to come.

10:30-11:00 am ET

Break

11:00–11:45 am ET

Understanding inference-time compute: Self-improvement and scaling

Akshay Krishnamurthy, Microsoft Research

Abstract

Inference-time compute has emerged as a new axis for scaling large language models, leading to breakthroughs in AI reasoning. Broadly speaking, inference-time compute methods involve allowing the language model to interact with a verifier to search for desirable, high-quality, or correct responses. While recent breakthroughs involve using a ground-truth verifier of correctness, it is also possible to invoke the language model itself or an otherwise learned model as verifiers. These latter protocols raise the possibility of self-improvement, whereby the AI system evaluates and refines its own generations to achieve higher performance. This talk presents new understanding of and new algorithms for language model self-improvement. The first part of the talk focuses on a new perspective on self-improvement that we refer to as sharpening, whereby we "sharpen" the model toward one placing large probability mass on high-quality sequence, as measured by the language model itself. We show how the sharpening process can be done purely at inference time or amortized into the model via post-training, thereby avoiding expensive inference-time computation. In the second part of the talk, we consider the more general setting of a learned reward model, show that the performance of naive-but-widely-used inference-time compute strategies does not improve monotonically with compute, and develop a new compute-monotone algorithm with optimal statistical performance. Based on joint works with Audrey Huang, Dhruv Rohatgi, Adam Block, Qinghua Liu, Jordan T. Ash, Cyril Zhang, Max Simchowitz, Dylan J. Foster and Nan Jiang.

11:45 am–12:30 pm ET

A Physics-informed Approach To Sensing

Petros Boufounos, Mitsubishi Electric Research Laboratories

Abstract

Physics-based models are experiencing a resurgence in signal processing applications. Thanks to developments in theory and computation it is now practical to incorporate models of dynamical systems within signal processing pipelines, learning algorithms and optimization loops. Advances in learning theory, such as Physics-Informed Neural Networks (PINNs) also allow for flexible and adaptive modeling of systems, even if the exact system model is not available at the algorithm design stage. In this talk we will explore how these new capabilities improve sensing systems and enable new capabilities is a variety of applications and modalities. We will discuss applications in underground imaging, fluid modeling and sensing, and airflow imaging, among others, and investigate different approaches to developing and using these models, their advantages and pitfalls.

12:30–2:00 pm ET

Lunch

2:00–2:45 pm ET

State of AI Reasoning for Theoretical Physics - Insights from the TPBench Project

Moritz Münchmeyer, University of Wisconsin-Madison

Abstract

Large-language reasoning models are now powerful enough to perform mathematical reasoning in theoretical physics at graduate or even research level. In the mathematics community, data sets such as FrontierMath are being used to drive progress and evaluate models, but theoretical physics has so far received less attention. In this talk I will first present our dataset TPBench (arxiv:2502.15815, tpbench.org), which was constructed to benchmark and improve AI models specifically for theoretical physics. I will analyze both the strengths and remaining weaknesses of LLMs in theoretical physics reasoning, and discuss strategies to improve their performance. I will then show our new results (arxiv:2506.20729) comparing different test-time scaling techniques on TPBench, including sequential and parallel reasoning methods combined with symbolic verification to boost performance.

2:45–3:30 pm ET

A model of emergent abilities in learning from language

Yasamin Bahri, Google DeepMind

Abstract

Abstract to come

3:30–4:00 pm ET

Closing

Speakers

Accommodations

We have established discounted rates for August 10–August 16, 2025 at the following hotels:

• Porter Square Hotel, 1924 Massachusetts Avenue, Cambridge, MA 02142.

  $235-275 nightly rate (1-2 people per room)

  Deadline to book: First come, first served

  To book, call 617-499-3399 and reference code 141315.

• Hotel 1868, 1868 Massachusetts Avenue, Cambridge, MA 02142.

  $225-265 nightly rate (1-2 people per room)

  Deadline to book: First come, first served

  To book, call 617-499-3399 and reference code 141315.

Workshop attendees are also welcome to book dorms for a discounted rate at Harvard University:

• Harvard University Dorms, 36 Oxford Street, Cambridge, MA 02138

  $110 nightly rate (1 person per room only)

  Book now

FAQ

• Who can attend the Summer Workshop? Any researcher working at or interested in the intersection of physics and AI is encouraged to attend the Summer Workshop.
• What is the cost to attend the Summer Worskhop? The registration fee for the Summer Workshop is 200 USD and includes a welcome dinner, as well as coffee breaks and snacks.
• If I come to the Summer School, can I also attend the Workshop? Yes! We encourage you to stay for the Workshop and you can stay in the dorms for both events if you choose (at your expense).
• Will the recordings of the talks be available? We plan to share the talks on our YouTube channel.

Submit a question or comment

2025 Organizing Committee

• Fabian Ruehle, Chair (Northeastern University)
• Bill Freeman (MIT)
• Cora Dvorkin (Harvard)
• Thomas Harvey (IAIFI Fellow)
• Sam Bright-Thonney (IAIFI Fellow)
• Sneh Pandya (Northeastern)
• Yidi Qi (Northeastern)
• Manos Theodosis (Harvard)
• Marshall Taylor (MIT)
• Marisa LaFleur (IAIFI Project Manager)
• Thomas Bradford (IAIFI Project Coordinator)