bio photo

Sharon Li

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

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About


I am an Assistant 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 as a postdoc researcher in the Computer Science department at Stanford University, working with the Hazy Group. I completed my PhD from Cornell University in 2017, where I was fortunate to be advised by John E. Hopcroft.


My passion and dream are to advance AI systems to a stage where they are not only intelligent but also safe and beneficial for humanity. Specifically, my lab focuses on algorithmic and theoretical foundations of reliable machine learning, addressing challenges in both model development and deployment in the open world. This involves detecting out-of-distribution data and learning novel structures and categories therein. Currently, our emphasis is on building responsible foundation models, such as large language models and vision-language models. We are driven to deeply understand how they work, when they fail, and effectively align them with human needs and desires. These research efforts are incredibly exciting since I believe the next decade of AI research will be shaped by considering both the human and machine perspectives in a more symbiotic manner, especially as we move towards more capable and advanced intelligent systems.


[Openings]: I am looking for creative and curiosity-driven minds to join my lab, with fully funded positions. The admission decision at UW-Madison is committee-based. For questions regarding the application process, please see the FAQ. I highly recommend reading my [Advising Statement] before you apply or email me.



Updates


5/16/2024: Yifei defended his thesis on "Reliable Foundation Models in the Open World" - congratulations Dr. Ming!
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.
9/12/2023: Honored to be named "Innovator of the Year" by MIT Technology Review, highlighted among the TR35 cohort this year.
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!
6/21/2023: Received NSF CAREER Award on "Foundations of Human-Centered Machine Learning in the Wild".
4/24/2023: 3 papers accepted by ICML. Congratulations team!
2/11/2023: Xuefeng received the inaugural Jane Street Graduate Research Fellowship.
12/14/2022: Grateful to receive the AFOSR Young Investigator (YIP) Award, sponsoring our work on "Human-Aligned Learning in the Open World (HALLOW)".
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!
8/26/2022: Received Amazon Research Award.
6/8/2022: Received American Family Funding Intiative Award.
5/15/2022: 4 papers accepted by ICML, congratulations to the team!
4/15/2022: Received ICLR Outstanding Paper Award Honorable Mention.

[Services]:

  • 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.

  • [Recent Talks]:

  • 5/13/2023: Invited talk at ICRA workshop "Back to the Future: Robot Learning Going Probabilistic"
  • 12/7/2023: Talk at Machine Learning Lunch Meeting (MLLM) at UW-Madison
  • 11/6/2023: Invited talk at TEDxUWMadison [Video]
  • 10/9/2023: Invited talk at Vanderbilt Machine Learning Seminar
  • 10/3/2023: Invited talk at ICCV'23 BRAVO Workshop
  • 10/2/2023: Invited talk at ICCV'23 AROW Workshop
  • 8/16/2023: Invited talk at University of Queensland Data Science Seminar
  • 7/28/2023: Mentor at Women in Machine Learning (WiML) Workshop
  • 6/30/2023: Panelist of AI from Foundations to Applications hosted by UW-Madison
  • 6/18/2023: Invited talk at CVPR'23 Workshop on Anomaly Detection
  • 5/6/2023: Invited talk at MIT conference on MI and AI safety
  • 4/14/2023: Invited talk at Computer Vision Round Table (CVRT) at UW-Madison
  • 4/12/2023: Invited talk at KAUST computer vision group
  • 2/10/2023: Invited talk at Cornell AI Seminar


  • [Teaching]:

  • 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