Rare AI Lab

PI: Aobo Li· Halıcıoğlu Data Science Institute (HDSI) · Department of Physics · UC SAN DIEGO aol002@ucsd.edu

Nature is full of Rare Physics Events of interest. To search for them, we must encode nature's message into experimental data with rare event search detector, and then decode them to unlock new physics and propel fundamental scientific understanding forward. My group leverage Artificial Intelligence (AI) as a powerful tool to decipher nature's messages.

I have an open PhD position in my group for 2025-2026 Application cycle. Prospective students are welcome to apply through either HDSI or Physics. Please email me for more details!

My Scientific/programmer profiles:

News:

Aobo Li serves as the Program Co-Chair of APS Global Physics Summit 2026 at Denver, Colorado
Aobo Li organized the second installment of APS DNP AI in Nuclear Physics Experiment Workshop!
Our workshop paper Efficient optimization of COHERENT detector design parameters with the Rare Event Surrogate Model was accepted by NeurIPS 2025 Machine Learning for Physical Science workshop! Congratulations to co-first authors Brian Liu and Sonata Simonaitis-Boyd.
Our paper TIDMAD: Time Series Dataset for Discovering Dark Matter with AI Denoising was accepted by NeurIPS 2025 as Spotlight!
Our group has submitted 2 papers to NeurIPS 2025! Check out the manuscript here 1. Gravitational Wave Surrogate Model 2. Dark Matter Dataset & Benchmarking.
Min Zhong started quantitative research internship at Nomura Securities.
Our new work RESuM: A Rare Event Surrogate Model for Physics Detector Design has been accepted by ICLR 2025 as Spotlight! Check out the manuscript at this Link.
Aobo Li delivered machine learning bootcamp at Tohoku University, Japan. Check out the video. about using LLM to build machine learning model!
Alex Migala attended the UCSD NSF A3D3 HDR Hackathon and won both the best-performing model award and most creative model award for the sea-level prediction challenge. Congratulation Alex!
Aobo Li was invited to give a talk on "AI in Neutrinoless Double-Beta Decay Experiment" on DNP 2024.
Sonata Simonaitis-Boyd volunteered on the UCSD HDR Hackathon challenge at UCSD.
Sonata Simonaitis-Boyd published a workshop paper on NeurIPS 2024 Machine Learning for Physical Science workshop, Congratulations Sonata! Link.
Alex Migala published a workshop paper on NeurIPS 2024 Machine Learning for Physical Science workshop, Congratulations Alex! Link.
Alex Migala presented a poster on Fast Machine Learning 2024 conference and won the best poster award. Congratulation Alex!
Sonata Simonaitis-Boyd organized the A3D3 Merchandise design context.
Sonata Simonaitis-Boyd organized the first A3D3 Undergraduate Research Symposium.
Avi Mehta, Sophie Wang, and Owen Yang won HDSI undergraduate research scholarship, congratulations to all of them!
Our new paper TIDMAD: Time Series Dataset for Discovering Dark Matter with AI Denoising has been submitted to NeurIPS 2024 Dataset & Benchmarking Track! Please check out our manuscript at this Link.
Eugene Ku has started as an Postbaccalaureate Research at Argonne National Lab. Congratulations Eugene! He will work with Dr. Varuni Sastry on deploying Vision Transformer at scale.
Sonata Simonaitis-Boyd will start as a new PhD student at HDSI in fall 2024, Congratulations Sonata! She has also joined A3D3 NSF HDR institute as a trainee affiliates.
Aobo Li was invited to deliver plenry talk on the Conference of Science at Sanford Underground Research Facilities (CoSSURF) 2024! The talk title is Pushing Rare Event Search to the Limit with Machine Learning Algorithms.
Aobo Li was invited to deliver plenry talk on the The 22nd International Workshop on Advanced Computing and Analysis Techniques in Physics Research! The talk title is Detecting Rare Events Using Artificial Intelligence.Link
Aobo Li was invited to A3D3 seminar at A3D3 NSF Harnessing Data Revolution Insittute! The colloquium title is Fast and Slow: AI in Rare Event Search.Link
Aobo Li hosted the AI in Nuclear Physics Experiment workshop at 2023 APS DNP-JPS Joint Meeting! 6 machine learning experts are invited to showcase their work.
Jessica T. Fry's paper was accepted by NeurIPS 2023 Machine Learning for Physical Science Workshop. The paper tile is "Long Time Series Data Release from Broadband Axion Dark Matter Experiment". Congratz Jessica! Link
Aobo Li was invited to give physics colloquium at South Dakota School of Mines and Technology!
Majorana Demonstrator has released its calibration data for AI/ML benchmarking purpose. These data are labeled short time series data produced by High-Purity Germanium (HPGe) Detector arrays. These data are accessible in HDF5 format here. For more information, please read the data release note.
Katharina Kilgus (U. Tuebingen, GeM group) will start summer summer research projects at UNC, under the joint mentor of Aobo Li and Julieta Gruszko. This trip is funded by German Reinhardt Frank-Stifung Foundation.
Aobo Li was invited to give physics division seminar at Argonne National Laboratory!
Aobo Li was invited to give panel talk in Carolina Data Science Now! Link
Henry Nachman (UNC, GeM group) graduated from UNC Chapel Hill with highest honor, congratulations Henry!
Bounds from KamLAND-Zen on neutrinoless double-beta decay begin to probe the heart of neutrino mass inverted hierarchy parameter space! Link Feature Article in Physics
KamNet has helped KamLAND-Zen reach the world's most sensitive search for 0𝑣ββ! Link

Research

Click here for selected publications

Nature

Neutrino

The "ghost particle" that travel through space at almost the speed of light and pass through ordinary matter almost undetected.

Dark Matter

Mysterious and elusive, dark matter is postulated to constitute about 85% of the matter in our universe.

Gravitational Wave

Ripples in space-time, created by massive cosmic collisions like black holes merging. They stretch and squeeze space as they travel through the universe.

Particle Physics Experiments

The LEGEND Experiment

Next-generation NLDBD experiment conducted at the INFN Gran Sasso National Laboratory (LNGS) in Italy, aiming at unambiguous discovery of NLDBD utilizing high purity Germanium detectors.

The KamLAND-Zen Experiment

The monolithic Xenon liquid scintillator detector experiment, carried out at the Kamioka Observatory in Japan, currently holds the world's most stringent limit in the search for neutrinoless double-beta decay (NLDBD).

The XENON Experiment

Employs a dual-phased time projection chamber detection system using liquid xenon to search for WIMP dark matter, carried out at the INFN Gran Sasso National Laboratory (LNGS) in Italy to push the boundaries of our understanding of the universe.

AI/ML Research

AI Agent

Scientific AI agents capable of autonomous discovery. These agents explore complex physics domains, identifying new particles and patterns that might elude traditional analyses, advancing fundamental science through intelligent automation.

Surrogate Model

Bayesian rare event surrogate models that drastically accelerate computationally expensive simulations in neutrino and gravitational wave physics. By replacing traditional physics simulators with fast neural networks, we enable rapid prototyping and inference.

Time Series Analysis

Developing advanced neural networks to analyze temporal data from various Rare Event Search Detectors. This includes extracting rich frequency content, denoising signals from quantum sensors, and uncovering dark matter signatures in ultra-long time series.

Fast Machine Learning on FPGA

Hardware-AI codesign for deploying machine learning models directly onto FPGAs and heterogeneous systems. This enables real-time data acquisition, particle position reconstruction, and intelligent detector control at the edge with microsecond latency.

Physics

KamLAND-Zen

With the help of KamNet, KamLAND-Zen 800 yields worlds most stringent limit on the effective Majorana mass and the first search for Majorana neutrino in the inverted ordering region!

Majorana Demonstrator

The Majorana Demonstrator, a predecessor experiment of LEGEND, recently unveiled its final results in 2023; LEGEND-200 is presently in the initial stages of collecting its first set of physics data.

XENONnT

Reported result from the first science run of the XENONnT dark matter experiment!

Group Members

Principle Investigator

Aobo Li (and Group Mascot Lulu)

Postdoctoral Scholar

Yue Ma

Graduate Students

Sonata Simonaitis-Boyd

Lucas Venetoulias

Akbota Assan

Undergraduate Students

Avi Mehta:

data science

Brian Liu:

data science

Matthew Segovia:

physics

Owen Yang:

data science

Sophie Wang:

data science

Liuxuhong (Francisco) Zhao:

data science

Luca Georgescu:

physics

Alumini & Visiting Scholar

Min Zhong:

Graduated, working as a quantitative analyst at Citadel Securities

Fong Vo:

Master student at USC

Owen Shi:

Master student at Columbia University

Ann-Kathrin Shuetz:

Postdoctoral Scholar at Lawrence Berkeley National Lab, visited Rare AI Lab in March 2024

Kelly Weerman:

PhD student at University of Amsterdam, visited Rare AI Lab in Feb. 2025

Eugene Ku:

UCSD undergraduate students worked in Rare AI Lab, currently PhD at Texas A&M

Alex Migala:

UCSD Physics PhD student, conducted first year research rotation at Rare AI lab

AI Tutorials

Introduction

Welcome to this AI/ML tutorial repository, developed during my faculty career. This tutorial series aims to provide interdisciplinary training, specifically designed to help physicists cross disciplinary borders into the world of Artificial Intelligence.

No prior AI/ML expertise is needed. We start from zero. During these tutorials, you will have the opportunity to move beyond theory and play with real data derived from cutting-edge physics detectors. You will learn how to build neural network models from scratch using this data.

Note: This is a growing repository. We plan to release more physics detector datasets and create additional tutorials in the future.

Content Overview

Datasets: Ranges from the BASIC (Majorana Demonstrator Data Release) to the ADVANCED (TIDMAD dataset for dark matter discovery).
Tutorials: Covers the essentials ("AI In a Nutshell"), coding cookbooks, using ChatGPT for ML, and two additional Machine Learning Course Series.

Suggested Learning Paths

The Essential Path (Quick Start):
BASIC DATASET → 1. AI In a Nutshell (run Jupyter Notebook) → 3. Using ChatGPT (update Jupyter Notebook)
The Full Exploration (Deep Dive):
BASIC DATASET → 1. Nutshell → 2. Cookbook → 3. ChatGPT → 4. Practical Series → 5. Bootcamp → ADVANCED DATASET.

Physics Datasets

Fig 1. Sample Waveforms

Majorana Demonstrator Data Release

BASIC DATASET High Purity Germanium Detector Signals

This dataset contains over 3 million data points derived from the Majorana Demonstrator experiment, which utilizes High Purity Germanium Detectors to search for Neutrinoless Double-Beta Decay. Each data point represents a real time-series waveform generated by the detector.

Input Data: 1D Numpy vector with 4000 samples (Time Series).
Labels: 4 Binary Classification labels + 1 Energy Regression label.
Goal: Train ML models to use the waveform as input to predict the analysis labels.
Format: HDF5 (See Tutorial 1 for reading instructions).

Download Dataset (Zenodo) Read Description (arXiv)

Fig 2. Noisy Input vs Ground Truth

TIDMAD: Dark Matter Discovery with AI Denoising

ADVANCED DATASET Quantum-enabled Axion Detector Data

Generated by a cutting-edge quantum-enabled lumped-element axion detector, this dataset targets the search for dark matter (85% of the universe's matter). It is an ultra-long time series dataset (>1 TB) containing 8-bit integer samples.

The Logic: We inject a clean dark-matter-like signal (Ground Truth) and read it out via a quantum sensor (Noisy Input). The Task: Build a Neural Network to denoise the readout and recover the injected signal.

Structure: HDF5 format. Split into Training (20 files), Validation (20 files), and Science (208 files).
Benchmark 1: Denoising Score (calculated on Validation Set).
Benchmark 2: Dark Matter Limit (Physics result calculated on Science Set).

High Difficulty: This dataset is extremely large (>1TB). Beginners are strongly suggested to complete other tutorials first. If starting here, please begin with a single training file.

Download & Scripts Benchmark Paper

Tutorial Series

1. AI In a Nutshell

How To Build a Machine Learning Model

This session teaches students how to build a machine learning model. The tutorial is designed based on the BASIC dataset, so users will learn how to read and analyze real physics data as part of the process.

We provide an overview of the full pipeline for building a deep neural network, and a Jupyter Notebook file hosted in google colab (credit: Hasung Song) that contains all codes in the pipeline.
Topics include: Reading and preprocessing data, neural network structure, back propagation, gradient descent, and classification vs. regression loss.

Slides Video Record Jupyter Notebook
2. Connecting Dots

An AI Cookbook

This lecture acts as an "AI Cookbook". Rather than aiming for a complete theoretical mastery of every algorithm, the goal is to show you "what exists" and "what X model can do for Y problem."

We expand on the framework established in Tutorial 1 to provide a broad overview of various AI/ML models, giving you the recipes you need to solve specific physics challenges.
Topics Covered:
- • Classical ML
- • Geometric Deep Learning
- • Time Series Models
- • Generative Models
- • Contrastive Learning
- • Model Interpretability
Lecture Slides Watch Video
3. Using ChatGPT to Build a ML Model

Modern AI Assistance & Prompt Engineering
Credit: Kelly Weerman

Don't spend months building complex models from scratch—ask an LLM to help you! This tutorial explores the art of Prompt Engineering and how to effectively communicate with Large Language Models to accelerate your physics research.

The Challenge: Starting with the simple neural network from Tutorial 1, you will learn how to guide ChatGPT to upgrade it into a complex Transformer model through a series of iterative conversations.
Key Topics:
- • Prompt Engineering
- • Code Refactoring with AI
- • From MLP to Transformers
Lecture Slides Watch Video
4. Practical Machine Learning Course Series

Comprehensive 5-Day Lecture Series

Created as a dedicated 5-day lecture series, this course is more theoretical and systematic than the previous tutorials. It is ideal for students who want to expand their scope and build a solid foundational understanding of AI/ML.
Curriculum Includes:
- • Basic Machine Learning
- • Computer Vision
- • Time Series Analysis
- • Advanced Machine Learning
View Full Course
5. Summer Bootcamp on Deep Learning

12-Day Intensive CS-Oriented Workshop
Co-created with Huajie Shao, Minghan Chen, Chenglin Miao, Mengdi Huai

Co-created with Computer Science faculties, this tutorial is more general and CS-oriented than the physics-focused series.

Spanning 12 days, it offers a rigorous, in-depth exploration of Deep Learning. The curriculum includes many non-physics tasks, making it excellent for students interested in core AI/ML methodologies.

Visit Course Website

Awards

Dissertation Award in Nuclear Physics

American Physical Society

"For the invention of a novel machine learning algorithm that broke down significant technological barriers with monolithic liquid scintillator detectors and, in turn, delivered the world’s most sensitive search for neutrinoless double beta decay." link

Oct. 2022
Postdoctoral Award of Research Excellence

UNC Chapel Hill

"Aobo Li is a postdoctoral research associate and COSMS Fellow in the College’s Department of Physics and Astronomy. He is described by his professors as a “rising star in the neutrino and nuclear physics community.” He leverages artificial intelligence to facilitate the experimental search of neutrino-less double-beta decay – part of an effort to explain why there was matter left after the Big Bang instead of only pure energy. His efforts to stay informed about new tools in machine learning (and immediately learn how to best apply them to experiments) has already led to several notable achievements. Li’s expertise and leadership provide a unique educational opportunity for UNC graduate and undergraduate students."link

July 2022
Machine Learning: Science and Technology Paper Award

NeurIPS 2022 Machine Learning and Physical Sciences Workshop

"The contribution Ad-hoc Pulse Shape Simulation using Cyclic Positional U-Net by Aobo Li, Julieta Gruszko, Brady Bos, Thomas Caldwell, Esteban León, and John Wilkerson was selected for the MLST Paper Award. The award is sponsored by Machine Learning: Science and Technology and acknowledges an outstanding submission to the workshop."link paper link

Nov. 2022

Rare AI Lab

Research

Click here for selected publications

Nature

Neutrino

Dark Matter

Gravitational Wave

Particle Physics Experiments

The LEGEND Experiment

The KamLAND-Zen Experiment

The XENON Experiment

AI/ML Research

AI Agent

Surrogate Model

Time Series Analysis

Fast Machine Learning on FPGA

Physics

KamLAND-Zen

Majorana Demonstrator

XENONnT

Group Members

Principle Investigator

Aobo Li (and Group Mascot Lulu)

Postdoctoral Scholar

Yue Ma

Graduate Students

Sonata Simonaitis-Boyd

Lucas Venetoulias

Akbota Assan

Undergraduate Students

Avi Mehta:

Brian Liu:

Matthew Segovia:

Owen Yang:

Sophie Wang:

Liuxuhong (Francisco) Zhao:

Luca Georgescu:

Alumini & Visiting Scholar

Min Zhong:

Fong Vo:

Owen Shi:

Ann-Kathrin Shuetz:

Kelly Weerman:

Eugene Ku:

Alex Migala:

AI Tutorials

Introduction

Content Overview

Suggested Learning Paths

Physics Datasets

Majorana Demonstrator Data Release

TIDMAD: Dark Matter Discovery with AI Denoising

Tutorial Series

1. AI In a Nutshell

2. Connecting Dots

3. Using ChatGPT to Build a ML Model

4. Practical Machine Learning Course Series

5. Summer Bootcamp on Deep Learning

Awards

Dissertation Award in Nuclear Physics

Postdoctoral Award of Research Excellence

Machine Learning: Science and Technology Paper Award

Teaching

PHYS 1B

DSC 291: Statistical Data Analysis in Physics

PHYS 139/239: Machine Learning in Physics

DSC40A: Theoretical Foundation of Data Science

The Practical Machine Learning

Appointment

Assistant Professor, Chancellor's Joint Initiative

CoSMS Fellow

Education

Boston University

University of Washington, Seattle