bio photo

Sharon Li

[CV]
Associate Professor
Department of Computer Sciences
University of Wisconsin-Madison
Office: CS5393

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About


I am an Associate Professor in the Department of Computer Sciences at the University of Wisconsin Madison. I am a member of machine learning@uw-madison and a faculty affiliate with the Data Science Institute. Previously I was a postdoc researcher in the Computer Science department at Stanford University, working with Christopher RĂ©. I completed my PhD from Cornell University in 2017, where I was fortunate to be advised by John E. Hopcroft.


My research focuses on algorithmic and theoretical foundations of safe and reliable AI, addressing challenges in both model development and deployment in the open world. This involves handling out-of-distribution data, quantifying uncertainty and learning novel structures and categories therein. Currently, our emphasis is on building responsible frontier models, such as large language models (LLMs) and multi-modal LLMs. We are driven to deeply understand how they work, when they fail, and ensure they serve human needs effectively and reliably.


My research has been recognized by the Alfred P. Sloan Fellowship (2025), MIT Innovators Under 35 Award (2023), NSF CAREER Award (2023), AFOSR Young Investigator (YIP) Award (2022), Forbes 30 Under 30 in Science (2020), and multiple faculty research awards from Google, Meta, and Amazon. I was named the "Innovator of the Year" by MIT Technology Review in 2023. Our research has also won the Outstanding Paper Award at NeurIPS 2022 and ICLR 2022.


[Openings]: In an effort to maintain a healthy group size, I have very limited PhD openings for the Fall 2026 admission cycle and will only consider applicants with an exceptional research fit. For questions regarding the application process, please see the FAQ. I strongly recommend reading my [Advising Statement] before applying or contacting me.



Updates


7/2025: Promoted to tenured professor.
7/1/2025: Received Google ML and Systems Faculty Award.
6/15/2025: Shawn received the NSF Graduate Research Fellowship.
5/16/2025: Xuefeng defended his Ph.D. thesis - congratulations Dr. Du!
5/1/2025: 5 papers accepted by ICML 2025.
3/3/2025: Will serve as the Program Chair for ICML 2026 in Seoul, South Korea.
12/3/2024: I will be co-organizing two workshops at ICLR 2025 on responsible AI and uncertainty quantification for LLM.
11/1/2024: Will be presenting 6 works at NeurIPS - see you in Vancouver!
5/1/2024: 4 papers accepted by ICML 2024, congratulations to Xuefeng, Froilan, Shawn and Yifei!
1/16/2024: 4 papers accepted by ICLR 2024.
7/19/2023: Yiyou defended his Ph.D. thesis - congratulations Dr. Sun!
6/26/2023: Received funding from ONR to support OOD detection research - thanks ONR!
4/24/2023: 3 papers accepted by ICML. Congratulations team!
2/11/2023: Xuefeng received the inaugural Jane Street Graduate Research Fellowship.
11/21/2022: Received NeurIPS Outstanding Paper Award.
11/11/2022: Excited to receive funding from Center for Advancing Safety of Machine Intelligence (CASMI), and partner on safe AI research.
11/2022: Received Google-Initiated Research Grant.
9/14/2022: 4 papers accpeted to NeurIPS 2022 and 1 paper accepted to NeurIPS 2022 Datasets and Benchmarks Track. Congrats to the team and co-authors!
5/15/2022: 4 papers accepted by ICML, congratulations to the team!
4/15/2022: Received ICLR Outstanding Paper Award Honorable Mention.

[Services]:

  • Program chair: ICML 2026
  • Area chair and senior program committee: NeurIPS, ICLR, ICML and AAAI.
  • Asscociate editor: ACM Transactions on Knowledge Discovery from Data (TKDD), Transactions on Machine Learning Research (TMLR)
  • Program chair and founding organizer: ICML Workshop on Uncertainty and Robustness in Deep Learning (UDL) 2019 & 2020.
  • Co-organizer: ICML Workshop on Uncertainty and Robustness in Deep Learning (UDL), 2021.
  • Co-organizer: ICML Workshop on Distribution-free Uncertainty Quantification (DFUQ), 2021.
  • Co-organizer: WiML Un-Workshop on Uncertainty Estimation, 2021.
  • Co-organizer: ICML Workshop on Distribution-free Uncertainty Quantification (DFUQ), 2022.
  • Co-organizer: NeurIPS Workshop on Robustness in Sequence Modeling, 2022.
  • Co-organizer: ICCV Tutorial on Reliability of Deep Learning for Real-World Deployment, 2023.
  • Co-organizer: CVPR Workshop on Prompting in Vision, 2024.
  • Co-organizer: DCAI: Data-centric Artificial Intelligence Workshop at WWW, 2024.
  • Co-organizer: ICLR Workshop on Quantify Uncertainty and Hallucination in Foundation Models: The Next Frontier in Reliable AI, 2025.
  • Co-organizer: ICLR Workshop on Advances in Financial AI: Opportunities, Innovations, and Responsible AI, 2025.



  • [Teaching]:

  • Spring 2025: CS540 Introduction to Artificial Intelligence
  • Fall 2023: CS762 Advanced Deep Learning
  • Fall 2022: CS762 Advanced Deep Learning
  • Spring 2022: CS540 Introduction to Artificial Intelligence
  • Fall 2021: CS762 Advanced Deep Learning
  • Spring 2021: CS540 Introduction to Artificial Intelligence
  • Fall 2020: CS839 Advanced Topics in Deep Learning.

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


    Sponsors We are thankful for the generous funding award and gift from the following sponsors: sponsor