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Sharon Yixuan Li

Assistant Professor
Department of Computer Sciences
University of Wisconsin-Madison

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I am an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin Madison. Previously I spent a wonderful year as a postdoc researcher in the Computer Science department at Stanford University, working with Chris Ré. I completed my PhD from Cornell University in 2017, where I was fortunate to be advised by John E. Hopcroft. I've spent time at Google AI as an intern, and Facebook AI as a Research Scientist. I have received Facebook Research Award, JPMorgan early-career faculty award, and was named Forbes 30 Under 30 in Science. I am currently a faculty fellow at the Madison Teaching and Learning Excellence (MTLE) program.

My broad research interests are in deep learning, a branch of machine learning. My time in both academia and industry has shaped my view and approach in research. My research develops algorithms and fundamental understandings to enable reliable open-world learning, which can function safely and adaptively in the presence of evolving and unpredictable data stream. Research topics that I am currently focusing on include:

  • Out-of-distribution detection for reliable ML;
  • Open-world machine learning.

  • News

    1/20/2022: Two papers accepted into ICLR 2022.
    12/3/2021: Will serve as an area chair for ICML 2022.
    12/1/2021: Two papers on OOD detection accepted into AAAI 2022.
    11/1/2021: Received Facebook Research Award.
    10/25/2021: Received gift funding from Google Brain.
    10/22/2021: We released a comprehensive survey on generalized OOD detection.
    10/18/2021: Received gift funding from Facebook.
    9/28/2021: Three papers on out-of-distribution detection accepted to NeurIPS 2021. Congrats to the team!
    7/22/2021: Paper on frequency-domain image translation (FDIT) accepted to ICCV 2021.
    7/15/2021: Received Madison Teaching and Learning Excellence Fellowship.
    6/18/2021: Paper on outlier mining for out-of-distribution detection is accepted to ECML 2021.
    6/4/2021: Received JPMorgan Chase early career outstanding faculty award.
    6/2021: Received American Family Funding Intiative Awards.
    4/4/2021: Will be co-organizing two ICML'21 workshops on uncertainty quantification.
    2/28/2021: Two papers on OOD detection accepted to CVPR 2021 (including one oral presentation).
    1/12/2021: Paper on Model Patching (for handling subgroup shift) accepted to ICLR 2021.
    10/28/2020: Will serve as a mentor at the Women in Machine Learning (WiML) workshop at NeurIPS 2020.
    9/25/2020: Paper on Energy-based Out-of-distribution Detection accepted to NeurIPS 2020.
    8/17/2020: Joined the CS Department at UW-Madison as an Assistant Professor. Here is a featured interview article.
    05/19/2020: Gave an invited talk on Out-of-distribution Uncertainty Estimation and Robustness in Open-World Machine Learning at Air Force Research Laboratory’s Workshop.
    4/23/2020: Selected Young Researcher to participate in the 8th Heidelberg Laureate Forum.
    3/14/2020: Will serve as Program Chair for ICML'20 workshop on Uncertainty and Robustness in Deep Learning.
    2/26/2020: Wrote a blog series on automating the art of data augmentation, featuring latest works on the practice, theory and new direction of data augmentation.
    12/3/2019: My talk is listed as one of 30 Influential AI Presentations in 2019.
    12/3/2019: Honored and thrilled to be featured in Forbes 30 Under 30 list in Science.
    3/8/2019: Honored to be featured in 30 Under 30 Leading Women in AI.
    2/24/2019: Paper on KNN-based robustness accepted as an oral presentation at CVPR 2019.
    2/24/2019: Will serve as Program Chair for ICML'19 workshop on Uncertainty and Robustness in Deep Learning.
    1/2019: Gave a talk at Deep Learning Summit San Francisco .
    5/2/2018: Research featured in a TechCrunch and Wired article.
    10/1/2018: Gave an invited talk at Microsoft Research AI in Redmond, WA.
    9/26/2018: Gave a talk at Grace Hopper Celebration (GHC) Artificial Intelligence track in Houston, TX.
    1/29/2018: Paper on ODIN for detecting out-of-distribution examples in neural networks accepted into ICLR 2018. .


  • Program chair: ICML workshop on Uncertainty and Robustness in Deep Learning (UDL) 2019 & 2020.
  • Area chair and senior program committee: NeurIPS, ICLR, ICML and AAAI.
  • Co-organizer: ICML workshop on Uncertainty and Robustness in Deep Learning (UDL) 2021.
  • Co-organizer: ICML workshop on Distribution-free Uncertainty Quantification (DFUQ) 2021.

  • [Recent/upcoming talks]:

  • 3/2022: AI Seminar at Oregon State University
  • 12/2021: NeurIPS'21 workshop on ImageNet: past, present, and future
  • 12/1/2021: PhysicsMeetsML seminar
  • 9/28/2021: Future of Data-Centric AI virtual event
  • 8/27/2021: Talk at Facebook
  • 8/21/2021: Keynote @ Artificial Intelligence for Anomalies and Novelties (AI4AN 2021)
  • 8/22/2021: Keynote @ Weakly-supervised Representation Learning (WSRL 2021 workshop)
  • 8/2/2021: IFDS workshop on Statistical Approaches to Understanding Modern ML Methods
  • 4/27/2021: MINDS & CIS Seminar at John Hopkins University
  • 4/17/2021: Women in Scientific Education and Research (WISER) at UW-Madison
  • 4/1/2021: Open Data Science Conference (ODSC) East 2021
  • 11/4/2020: Women in Computer Science (WiCS) Seminar at Stanford
  • 10/30/2020: Information Systems Seminar at UC Berkeley
  • 10/28/2020: SILO Seminar at UW-Madison. [Recording]
  • 9/21/2020: MLOS Seminar at UW-Madison+Microsoft. [Recording]

  • [Teaching]:

  • Fall 2021: CS762 Advanced Deep Learning
  • Spring 2021: CS540 Introduction to Artificial Intelligence .
  • Fall 2020: CS839 Advanced Topics in Deep Learning (a newly developed graduate-level course).

  • Misc
    I travel and occasionally take photos. Here is my pictorial Travel Memo. This is the treasure I shoot with.