Rare AI Lab

PI: Aobo Li · Halıcıoğlu Data Science Institute (HDSI) · Department of Physics · UC SAN DIEGO aol002@ucsd.edu
Nature is full of incredible Rare Physics Events of interest. To search for these rare events, we must encode messages of nature into experimental data with Rare Event Search Detector, and subsequently decode them to unlock new physics and propel fundamental scientific understanding forward. My group delve into the realm of Artificial Intelligence (AI) as a conduit for deciphering nature's messages. To delve deeper into our work, I invite you to explore the Research tab on the left and also peruse this interview article.
My Scientific/programmer profiles:
News:
  • Our new work RESuM: A Rare Event Surrogate Model for Physics Detector Design has been accepted by ICLR 2025! Check out the manuscript at this Link.
  • 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.
  • Sophie Wang, Avi Mehta, 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.
  • Sophie Wang has started as an summer research intern at Tsing-Hua University. Congratulations Sophie! She will work with Dr. Benda Xu on liquid scintillator event fitting.
  • 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

Particle Physics Experiments

AI/ML Research

Rare AI

Traditional, data-hungry AI algorithms face unique challenges in searches searching for rare physics events: rare events are, by definition, exceptionally scarce. Moreover, since potential discoveries could represent Nobel Prize-level breakthroughs, the AI models must maintain rigorous statistical validity and complete transparency. Our group aims to Rare AI algorithms for specific challenges in particle physics: Bayesian Rare Event Surrogate Models that accelerate expensive simulation, specialized foundation models that enable autonomous discovery, and counterfactual fairness techniques that ensure robust validation of potential discoveries.

Geometric Deep Learning

Different Rare Event Search Detectors generate distinct data signatures requiring specialized neural architectures: LEGEND's germanium detectors produce sharp, brief pulses; ABRACADABRA's SQUIDs generate massive time series with rich frequency content; KamLAND-Zen and XENONnT create multidimensional spatiotemporal patterns; 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



Group Members


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

Sept. 2015 - Sept. 2020

University of Washington, Seattle

Bachelor of Science
Major: Physics

Advisor: Jason Detwiler

Sept. 2011 - June 2015
IP Address