Shibo Li 栗识博

Ph.D. Candidate in Computer Science

Scientific Computing and Imaging Institute

About Me

I will join the Department of Computer Science at Florida State University(FSU) as a tenure-track Assistant Professor in Fall 2024. I am looking for self-motivated students to work on interdisciplinary projects in the landscape of AI for Science. Please see the current openings below.

I am a Ph.D. Candidate in Computer Science both affliated with The Kahlert School of Computing (SoC) and Scientific Computing and Imaging Institute (SCI) at The University of Utah. I received my M.S. degree at University of Pittsburgh and my B.E. degree at South China University of Technology(SCUT). I also spent time at Schlumberger-Doll Research as robotics research intern and at Amazon as applied scientist intern working on privacy-preserving learning algorihtms and in-context few-shots learning with pretrained large language/multi-modality models.

My research vision is deeply rooted in AI for Science — by leveraging state-of-the-art learning techniques to facilitate the foundamental and classical studies of scientific priciples from complex, structural, and high-dimensional data. I am advised by Dr. Shandian Zhe.

My interests include but not limit to

Probabilistic Learning : Bayesian modeling, Approximate Inference, Uncertainty quantification of deep models
AI for Science : Surrogate Modeling, Operator Learning, Physical-Informed Machine Learning
Multi-Objective Learning : Multi-Task Learning, Multi-Fidelity Learning, Transfer Learning, Meta-Learning
Interactive Machine Learning : Bayesian Optimisation, Active Learning, Multi-Armed Bandits, Reinforcement Learning

News

[May 1st 2024] I will join FSU as a tenure-track assitant professor in the Department of Computer Science starting Fall 2024.

Openings

I am looking for highly motivated students with a solid background in probability, linear algebra, and/or optimization and good programming skills. If you want to join my group, please contact me via shiboli.cs@gmail.com. In particular, if you have a strong math and/or physics background (e.g., with a BS degree in math or physics) and have coding experiences, e.g., Matlab and Python), feel free to schedule a meeting with me.

Note: Please title you email in the format of [Phd/Master/Undergraduate/Interns - YOUR NAME - STARTING TERM] to get noticed.

Ways to work with me

[ Applying Ph.D. ]: Please send me your CV, transcript, TOEFL, GRE scores (if applicable), and any other materials you think would be helpful. More information could be found via this link.
Applications for Fall 2024 and Spring 2025 are still open; contact me before applying.
[ Existing master, undergraduate students ]: If you are a master’s or undergraduate student who has already been affiliated with FSU and want to expose yourself to state-of-the-art machine learning research, please send me your CV/Resume to my email.
[ Reseach Interns ]: Feel free to contact me.

Publications

Zheng Wang, Shibo Li, Shikai Fang, Shandian Zhe (2024). Diffusion-Generative Multi-Fidelity Learning for Physical Simulation. ( Preprint ).

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Shibo Li, Xin Yu, Wei Xing, Mike Kirby, Akil Narayan, Shandian Zhe (2024). Multi-Resolution Active Learning of Fourier Neural Operators. In The 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024) — Oral presentation .

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Shikai Fang, Xin Yu, Zheng Wang, Shibo Li, Mike Kirby, Shandian Zhe (2024). Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data. In Twelfth International Conference on Learning Representations (ICLR 2024).

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Shikai Fang, Madison Cooley, Da Long, Shibo Li, Mike Kirby, Shandian Zhe (2024). Solving High Frequency and Multi-Scale PDEs with Gaussian Processes. In Twelfth International Conference on Learning Representations (ICLR 2024).

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Zheng Wang, Shikai Fang, Shibo Li, Shandian Zhe (2023). Dynamic Tensor Decomposition via Neural Diffusion-Reaction Processes. In Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023) — Spotlight top 10% .

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Shikai Fang, Xin Yu, Shibo Li, Zheng Wang, Mike Kirby, Shandian Zhe (2023). Streaming Factor Trajectory Learning for Temporal Tensor Decomposition. In Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023).

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Shibo Li, Michael Penwarden, Yiming Xu, Conor Tillinghast, Akil Narayan, Mike Kirby, Shandian Zhe (2023). Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks. In The 40th International Conference on Machine Learning (ICML 2023).

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Shibo Li, Li Shi, Shandian Zhe (2023). Infinite-Fidelity Surrogate Learning via High-order Gaussian Processes. In 1st Workshop on the Synergy of Scientific and Machine Learning Modeling @ ICML 2023.

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Shibo Li, Zheng Wang, Akil Narayan, Mike Kirby, Shandian Zhe (2023). Meta-Learning with Adjoint Methods. In The 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023).

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Shibo Li, Zheng Wang, Mike Kirby, Shandian Zhe (2022). Infinite-Fidelity Coregionalization for Physical Simulation. In Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022).

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Shibo Li, Jeff Phillips, Xin Yu, Mike Kirby, Shandian Zhe (2022). Batch Multi-Fidelity Active Learning with Budget Constraints. In Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022).

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Shibo Li, Mike Kirby, Shandian Zhe (2022). Decomposing Temporal High-Order Interactions via Latent ODEs. In The 39th International Conference on Machine Learning (ICML 2022).

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Zheng Wang, Yiming Xu, Conor Tillinghast, Shibo Li, Akil Narayan, Shandian Zhe (2022). Nonparametric Embeddings of Sparse High-Order Interaction Events. In The 39th International Conference on Machine Learning (ICML 2022).

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Shibo Li, Zheng Wang, Mike Kirby, Shandian Zhe (2022). Deep Multi-Fidelity Active Learning of High-Dimensional Outputs . In The 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022).

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Shibo Li, Mike Kirby, Shandian Zhe (2021). Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks. In Thirty-Fifth Annual Conference on Neural Information Processing Systems (NeurIPS 2021).

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Shibo Li, Wei Xing, Mike Kirby, Shandian Zhe (2020). Multi-Fidelity Bayesian Optimization via Deep Neural Networks. In Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2020).

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Shibo Li, Wei Xing, Mike Kirby, Shandian Zhe (2020). Scalable Variational Gaussian Process Regression Networks. In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI 2020).

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Tao Yang, Shikai Fang, Shibo Li, Yulan Wang, Qingyao Ai (2020). Analysis of Multivariate Scoring Functions for Automatic Unbiased Learning to Rank. In The 29th ACM International Conference on Information and Knowledge Management ( CIKM 2020 ).

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Services

Program Committee: UAI 2024, AISTATS 2024, UAI 2023, AISTATS 2023, UAI 2022, ICMLA 2022
Conference Reviewer: ICML 2024, ICLR 2024, NeurIPS 2023, ICML 2023, NeurIPS 2022, ICML 2022, AISTATS 2021, ICMLA 2021, UAI 2021, AAAI 2020

Teaching

Coming soon!

Contact

shiboli.cs@gmail.com
3910, 50 Central Campus Drive, Salt Lake City, UT 84112