AI for Rare Event Lab · PI: Aobo Li · UC SAN DIEGO Email
Nature is full of incredible Rare Physics Events that we want to study, but nature doesn't speak English. To search for these rare events, we must encode messages of nature into experimental data 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. We are currently hiring enthuiastic students from both Physics and Data Science to pursue a PhD degree in the search for rare events!
My Scientific/programmer profiles:
  • 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


Click here for selected publications




Self-Supervised Learning (SSL)

By training on large, unlabelled datasets, SSL models acquire a task-agnostic representation of detector data. Key for unlocking the "ChatGPT" of particle physics experiments.

Fast ML on FPGA

Design and deploy cutting-edge machine learning models onto the next-generation data acquisition board, aiming at empower KamLAND-Zen with in-situ position reconstruction capabilities and real-time detector control functionality.

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.


Harness breakthroughs in geometric deep learning and spatiotemporal data analysis to maximally extract information from KamLAND-Zen data.

Transfer Learning

Leveraging transfer learning algorithms, we can "translated" simulated Germanium detector pulses to a state indistinguishable from realistic detector pulse.


Group Members




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


Sept. 2015 - Sept. 2020

University of Washington, Seattle

Bachelor of Science
Major: Physics

Advisor: Jason Detwiler

Sept. 2011 - June 2015
IP Address