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 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

Rare AI

Traditional data-intensive AI algorithms struggle to adapt to rare physics event searches due to limited training data availability. The overarching goal of the Rare AI Lab is to develop specialized AI algorithms that operate effectively in sparse-data domains, thereby maximizing the discovery potential of rare event search experiments. My research interests include:

  • Bayesian rare event surrogate models that accelerate computationally expensive simulations in neutrino and gravitational wave physics
  • AI-ready datasets derived from real neutrino and dark matter detectors
  • Scientific AI agents capable of autonomous discovery
  • Particle identification using geometric deep learning methods

Geometric Deep Learning

Different Rare Event Search Detectors generate distinct datatype: LEGEND's germanium detectors produce sharp, brief pulses (TOP); ABRACADABRA's SQUID (superconducting quantum interference device) generate ultra-long time series with rich frequency content (MIDDLE); KamLAND-Zen and XENONnT create high-dimensional spatiotemporal data projected onto a plane or a sphere (BOTTOM); and LIGO detects unique chirp-like gravitational wave signals. Our group develops neural networks that leverage the inherent structure and symmetries of each data type. We create targeted benchmarking datasets that connect algorithm performance directly to each experiment's specific challenges, ensuring our innovations translate into meaningful scientific discoveries.

Hardware-AI Codesign

Our research focuses on deploying ML models for real-time data acquisition in particle physics experiments. We're collaborating with experts on FPGA and heterogeneous system to integrate advanced neural networks onto RFSoC (Radio Frequency System-on-Chip) platforms, enabling KamLAND-Zen to reconstruct particle positions and control detector functions in real-time.

Physics Data Release for AI/ML

Releasing real neutrino and dark matter detector data to the public, allowing core AI algorithms to extract the signal and produce real physics results thereby advancing fundamental science!

GeM Group

The Germanium Machine Learning (GeM) Group within the LEGEND collaboration, leverages efficient and interpretable AI to aid all aspects of LEGEND analysis while educating domestic and international collaborators to gain AI experiences.

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)

    Graduate Students

    Sonata Simonaitis-Boyd

    Min Zhong

    Akbota Assan

    Undergraduate Students

    Avi Mehta:
    data science
    Brian Liu:
    data science
    Fong Vo:
    data science
    Matthew Segovia:
    physics
    Owen Shi:
    data science
    Owen Yang:
    data science
    Sophie Wang:
    data science
    Liuxuhong (Francisco) Zhao:
    data science
    Luca Georgescu:
    physics

    Alumini & Visiting Scholar

    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

Majorana Waveform Example
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)
TIDMAD Denoising Example
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

Awards

Teaching

Appointment

Assistant Professor, Chancellor's Joint Initiative

Halıcıoğlu Data Science Institute & Department of Physics
UC San Diego
Sept. 2023 - Present

CoSMS Fellow

UNC Chapel Hill

Advisor: Julieta Gruszko

Sept. 2020 - Aug. 2023

Education

Boston University

Doctor of Philosophy
Major: Physics

Advisor: Chritopher Grant

Thesis: The Tao and Zen of neutrinos: neutrinoless double beta decay in KamLAND-Zen 800
Sept. 2015 - Sept. 2020

University of Washington, Seattle

Bachelor of Science
Major: Physics

Advisor: Jason Detwiler

Sept. 2011 - June 2015
IP Address