AI Winter School 2025 - CFPU/Brown University Department of Physics

Contribution List

3. Development and Deployment of Graph Neural Networks in Particle Physics

Lazar Novakovic, Loukas Gouskos (Brown University)

In recent years, Graph Neural Networks (GNNs) have emerged as a transformative tool in particle physics, offering a powerful framework for analyzing complex, non-Euclidean data structures such as particle interactions and detector outputs. This session will provide an exploration of GNNs, bridging their theoretical foundations with practical applications in particle physics. We will highlight...

7. Introduction to Generative AI with Industrial Applications

Michael Luk

This module will introduce fundamental concepts of Artificial Intelligence; provide an overview of Generative AI and its applications; offer hands-on experience with AI and Gen AI models and tools; and highlight industrial and physics-related applications.

1. Introductory Module

Shawn Dubey

This module is the introductory module to the virtual AI Winter School. It will consist of a short introductory lecture to AI/ML and then a brief hands-on session that will introduce participants to the basics of using Google Colab, the required platform for this winter school. This module is particularly geared toward those with no knowledge of AI/ML and is recommended as a prerequisite to...

2. Machine Learning for Data Compression in Dark Matter Direct Detection Experiments

Shawn Dubey, Woody Hulse

The LUX-ZEPLIN (LZ) dark matter direct detection experiment searches directly for dark matter particle interactions in a 10-tonne liquid xenon target. LZ collects about 50 MBytes per second of detector data across its 494 photomultiplier tubes (PMTs), or around 1.5 PBytes (1.5 million GB) per year. The utility of this data naturally becomes constrained by its scale–to efficiently analyze this...

5. Overview of Large Language Models (LLMs) and RAG

James Verbus

This module will provide an exploration of LLM tools and techniques, including using OpenAI API, an open LLaMA model, and retrieval-augmented generation (RAG) for improving AI system performance with external data. We will set up an LLM using both OpenAI’s API and a locally installed LLaMa model, and build a basic RAG system to demonstrate how external data can be leveraged to enhance AI...

4. Physics-Inspired Operator Learning for Inverse Scattering with Application to Ground Penetrating Radar

Yanting Ma (Mitsubishi Electric Research Laboratories (MERL))

Ground Penetrating Radar (GPR) provides a non-destructive solution for underground utility mapping. The data acquisition process involves emitting known electromagnetic wave into the subsurface and recording the scattered wave above the ground. The goal for inverse scattering is to estimate the spatial distribution of the electric permittivity of the subsurface based on the received scattered...

6. Using Unsupervised Learning to Find Interacting and Starburst Galaxies

Ian Dell'Antonio (Brown University)

Galaxy interactions are laboratories for dark matter physics, star formation and galaxy evolution. Interacting and starburst galaxies can be detected in deep imaging surveys but represent a small fraction of the tens of millions of galaxies. Furthermore, interactions occur in many different scenarios and have different signatures, such as tidal tails, resonances or rings, and disrupted disks,...