Zhuoyuan Jacob Wang

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Hi, I’m a postdoctoral researcher in Computing and Mathematical Sciences at California Institute of Technology, working with Prof. Anima Anandkumar. I received my Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University, advised by Prof. Yorie Nakahira. I earned my bachelor’s degree from Tsinghua University, advised by Prof. Gao Huang and Prof. Yilin Mo.

My research interests lie at the intersection of control theory and scientific machine learning, with a focus on safety-critical control, physics-informed learning, stochastic systems and robotics.

Contact: jacobw [at] caltech.edu
Follow: Google Scholar | LinkedIn | jacobwang925

news

Aug 23, 2026 Our paper “OpInf-LLM: Parametric PDE Solving with LLMs via Operator Inference” was accepted to Transactions on Machine Learning Research (TMLR) with Featured Certification!
Apr 23, 2026 PI-BSNet and OpInf-LLM will be presented as posters at the ICLR 2026 AI + PDE Workshop!
Feb 20, 2026 Our paper “Physics-Informed Deep B-Spline Networks” was accepted to Transactions on Machine Learning Research (TMLR)!
Jan 22, 2026 Our paper “Online Adaptive Probabilistic Safety Certificate with Language Guidance” was accepted to L4DC 2026!
Dec 23, 2025 Our paper “Myopically Verifiable Probabilistic Certificates for Safe Control and Learning” was accepted to IEEE Transactions on Automatic Control (TAC)!

selected publications

  1. CDC 2025
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    Neural spline operators for risk quantification in stochastic systems
    Zhuoyuan Wang, Raffaele Romagnoli , Kamyar Azizzadenesheli , and Yorie Nakahira
    2025 IEEE 64th Conference on Decision and Control (CDC) , 2025
    arXiv Code
  2. AAAI 2024
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    Physics-Informed Representation and Learning: Control and Risk Quantification
    Zhuoyuan Wang, Reece Keller , Xiyu Deng , Kenta Hoshino , Takashi Tanaka , and Yorie Nakahira
    Proceedings of the AAAI Conference on Artificial Intelligence , 2024
    arXiv Code
  3. ICRA 2024
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    Towards Proactive Safe Human-Robot Collaborations via Data-Efficient Conditional Behavior Prediction
    Ravi Pandya , Zhuoyuan Wang, Yorie Nakahira , and Changliu Liu
    2024 IEEE International Conference on Robotics and Automation (ICRA) , 2024
    arXiv Slides
  4. L4DC 2023
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    A Generalizable Physics-Informed Learning Framework for Risk Probability Estimation
    Zhuoyuan Wang, and Yorie Nakahira
    Learning for Dynamics and Control Conference , 2023
    arXiv Code
  5. ACC 2022
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    Myopically Verifiable Probabilistic Certificate for Long-Term Safety
    Zhuoyuan Wang, Haoming Jing , Christian Kurniawan , Albert Chern , and Yorie Nakahira
    2022 American Control Conference (ACC) , 2022
    arXiv Code
  6. L-CSS
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    Scalable Long-Term Safety Certificate for Large-Scale Systems
    Kenta Hoshino , Zhuoyuan Wang, and Yorie Nakahira
    IEEE Control Systems Letters, 2023
    PDF
  7. TPAMI
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    Self-Supervised Discovering of Interpretable Features for Reinforcement Learning
    Wenjie Shi , Gao Huang , Shiji Song , Zhuoyuan Wang, Tingyu Lin , and Cheng Wu
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020
    arXiv