Shibo Li (栗识博)

Assistant Professor,
Department of Computer Science,
Florida State University (FSU),
shiboli@cs.fsu.edu

prof_pic.jpg

206A James Jay Love Building (LOV)

1017 Academic Way

Tallahassee, FL 32304

Research: My primary research area, AI for Science, integrates physical system analysis with machine learning methodologies. Computational physics, developed over centuries, is essential for understanding the universe and creating new technologies. Meanwhile, AI has revolutionized many sectors. Though these fields may seem distinct, they are complementary: physical insights improve data-driven methods’ efficacy, and data-driven methods capture physical laws flexibly. Leveraging abundant data, these methods provide statistical insights, augment traditional research, and offer efficient computing infrastructure for enhanced efficiency.

My research interests include but not limited to

  • probabilistic machine learning and modeling, approximate inference
  • surrogate modeling, operator learning, physics-informed machine learning
  • multi-task learning, meta learning, transfer learning
  • interactive machine learning and optimization

Education: I obtained my Ph.D. degree in Computer Science from The Kahlert School of Computing (SoC) at The University of Utah. During my Ph.D study, I was honorly advised by Dr. Shandian Zhe. I received my M.S. degree from University of Pittsburgh and my B.E. degree from South China University of Technology (SCUT).

FYI: I am avtively looking for self-motivated students to work on interdisciplinary projects in the landscape of AI for Science. Please see the details in here.

news

Aug 06, 2024 I joined the Department of Computer Science @ FSU as an Assistant Professor

selected publications

[full list]
  1. AISTATS
    Multi-Resolution Active Learning of Fourier Neural Operators
    Shibo Li, Xin Yu, Wei Xing, Mike Kirby, Akil Narayan, and Shandian Zhe
    In The 27th International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
  2. AISTATS
    Meta-Learning with Adjoint Methods
    Shibo Li, Zheng Wang, Akil Narayan, Robert Kirby, and Shandian Zhe
    In Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, 2023
  3. NeurIPS
    Infinite-Fidelity Coregionalization for Physical Simulation
    Shibo Li, Zheng Wang, Robert Kirby, and Shandian Zhe
    In The Thirty-sixth Annual Conference on Neural Information Processing Systems, 2022
  4. AISTATS
    Deep Multi-Fidelity Active Learning of High-Dimensional Outputs
    Shibo Li, Zheng Wang, Robert Kirby, and Shandian Zhe
    In The 25th International Conference on Artificial Intelligence and Statistics, 2022
  5. NeurIPS
    Multi-fidelity Bayesian optimization via deep neural networks
    Shibo Li, Wei Xing, Robert Kirby, and Shandian Zhe
    In Thirty-fourth Annual Conference on Neural Information Processing Systems, 2020