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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 University of Wisconsin Madison. I have recently spent a wonderful year as a postdoc researcher in the Computer Science department at Stanford University, working with Chris Ré. Previously, I completed my PhD from Cornell University in 2017, where I was fortunate to be advised by John E. Hopcroft. My thesis committee members are Kilian Q. Weinberger and Thorsten Joachims. I've spent time at Google AI twice as an intern, and Facebook AI as a Research Scientist. I was named Forbes 30 Under 30 in Science in 2020.

My broad research interests are in deep learning and its applications. My time in both academia and industry has shaped my view and approach in research. The goal of my research is to enable transformative algorithms and practices towards trustworthy open-world machine learning, which can function safely and adaptively in the presence of evolving and unpredictable data stream. Our works explore, understand, and mitigate the many challenges where failure modes can occur in deploying machine learning models in the open world. Research topics that I am currently focusing on include:

  • Uncertainty estimation and out-of-distribution detection in deep learning;
  • Robustness to data irregularity and out-of-distribution generalization;
  • Applications of uncertainty-aware deep learning in healthcare and computer vision.

  • [Teaching]: I am teaching a new grad-level course CS839 Special Topics in Deep Learning this fall.

    [Upcoming talks]:

  • 9/21/2020: Microsoft + UW Madison, ML-optimized Systems Seminar
  • 10/28/2020: SILO Seminar at UW-Madison
  • 10/30/2020: Information Systems Seminar at UC Berkeley

  • News

    8/18/2020: Will serve as a Senior Program Committee member for AAAI 2021.
    8/18/2020: Our recent work on Model Patching is released.
    8/17/2020: Joined the CS Department at UW-Madison as an Assistant Professor. Here is a featured interview article.
    8/15/2020: Check out our latest ECCV workshop paper on Beyond the Pixels: Exploring the Effect of Video File Corruption on Model Robustness.
    8/7/2020: Will serve as a Senior Program Committee member for IJCAI 2021.
    7/9/2020: Will serve as an area chair for ICLR 2021.
    6/26/2020: Our latest work on robust out-of-distribution detection via informative outlier mining is released.
    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.
    3/25/2020: Will serve as an area chair for NeurIPS 2020.
    3/23/2020: Our latest work on robust out-of-distribution detection is released.
    3/14/2020: Will serve as Program Chair for ICML'20 workshop on Uncertainty and Robustness in Deep Learning.
    3/2/2020: As part of the Hazy Research team, we received Stanford HAI-AWS Grant to work on model patching with learned data augmentations.
    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.
    12/20/2019: Excited that our research (with Karan Goel) on model patching is funded by Salesforce Deep Learning Grant.
    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.
    8/25/2019: Will serve as Senior Program Committee for AAAI 2020.
    8/26/2019: Started my postdoc at Stanford Computer Science Department.
    4/28/2019: Paper on large-scale weakly supervised learning for e-commerce search accepted to KDD 2019.
    3/8/2019: Honored to be featured in 30 Under 30 Leading Women in AI.
    2/24/2019: Paper on adversarial defense using KNN 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.
    12/13/2018: I will be giving a talk at Deep Learning Summit San Francisco in Jan 2019.
    10/1/2018: Gave an invited talk at Microsoft Research AI in Redmond, WA.
    9/27/2018: Served on a panel of Women in Research at Facebook.
    9/26/2018: Gave a talk at Grace Hopper Celebration (GHC) Artificial Intelligence track in Houston, TX.
    7/3/2018: Paper on exploring the limits of weakly supervised pretraining accepted into ECCV 2018.
    5/12/2018: Paper on understanding the loss surface of neural networks accepted into ICML 2018.
    5/2/2018: Research work at Facebook was featured in a TechCrunch and Wired article.
    4/3/2018: Received CVPR'18 Doctoral Consortium travel award.
    3/13/2018: Served on a panel at Facebook's Women in Research Lean In (WiRL) Circle.
    1/29/2018: Paper on detecting out-of-distribution examples in neural networks accepted into ICLR 2018.
    11/25/2017: Received ACM-W Scholarship in 2017.
    10/16/2017: I will be presenting at Women in Machine Learning (WiML) workshop in December this year.
    10/2017: Gave a talk at Grace Hopper Celebration (GHC) Artificial Intelligence track in Orlando, FL.
    8/5/2017: Selected as one of the Rising Stars in EECS 2017 by Stanford University.
    6/6/2017: Paper accepted for publication in Transactions on Knowledge Discovery from Data (TKDD).
    3/12/2017: Received ICLR 2017 Student Travel Award.
    2/27/2017: Paper on StackedGAN has been accepted into CVPR 2017.
    2/6/2017: Paper on Snapshot Ensembles has been accepted into ICLR 2017.

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