I am a PhD Candidate at UC San Diego department of Computer Science and Engineering advised by Prof. Rose Yu. I received my Bachelor degree in Mathematics from Huazhong University of Science and Technology and Master degree in Data Science from Northeastern University.

My research primarily lies in deep learning, especially for spatiotemporal data. In particular, I’m interested into building accurate, interpretable and generalizable deep forecasting models for large-scale real-world data. My works have been applied to forecasting spatiotemporal systems in finance, epidemiology, healthcare, traffic and physical sciences. For more details, see my CV and google scholar.

I will join Prof. Tess Smidt’s group as a postdoctoral researcher at MIT in August.

Selected Publications

  • Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts
  • Rui Wang, Yihe Dong, Sercan O Arik, Rose Yu.
    International Conference on Learning Representations (ICLR) 2023
  • Meta-Learning Dynamics Forecasting Using Task Inference
  • Rui Wang*, Robin Walters*, Rose Yu.
    Advances in Neural Information Processing Systems (NeurIPS) 2022
  • Physics-Guided Deep Learning for Dynamical Systems, A Survey
  • Rui Wang, Rose Yu.
    In submission to ACM Computing Survey
  • Approximately Equivariant Networks for Imperfectly Symmetric Dynamics
  • Rui Wang*, Robin Walters*, Rose Yu.
    International Conference on Machine Learning (ICML) 2022
  • Latent Space Simulation for Carbon Capture Design Optimization.
  • Brian Bartoldson, Rui Wang, Yucheng Fu, David Widemann, Sam Nguyen, Jie Bao, Zhijie Xu, Brenda Ng.
    Annual Conference on Innovative Applications of Artificial Intelligence (IAAI) 2022
  • Incorporating Symmetry into Deep Dynamics Models for Improved Generalization
  • Rui Wang*, Robin Walters*, Rose Yu.
    International Conference on Learning Representations (ICLR) 2021
  • Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems
  • Rui Wang, Danielle Maddix, Christos Faloutsos, Yuyang Wang, Rose Yu.
    Annual Conference on Learning for Dynamics and Control (L4DC) 2021
  • Physics-based vs. Data-driven A Benchmark Study on COVID-19 Forecasting.
  • Rui Wang, Danielle Maddix, Christos Faloutsos, Yuyang Wang, Rose Yu.
    Best Paper Award at NeurIPS, Machine Learning in Public Health Workshop 2020
  • Towards Physics-informed Deep Learning for Turbulent Flow Prediction
  • Rui Wang, Karthik Kashinath, Mustafa Mustafa, Adrian Albert, Rose Yu.
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2020
  • Aortic Pressure Forecasting with Deep Sequence Learning
  • Rui Wang*, Eliza Huang*, Uma Chandrasekaran, Rose Yu.
    Computing in Cardiology 2020


    Research Intern

    2022.06 - 2022.10
    Google Cloud AI, Sunnyvale, CA; supervised by Yihe Dong and Sercan O.Arik.

    Machine Learning Research Intern

    2021.06 - 2021.09
    Lawrence Livermore National Laboratory, Livermore, CA; supervised by Brenda Ng.

    Applied Scientist Intern

    2020.05 - 2020.08
    Amazon Web Services, Palo Alto, CA; supervised by Danielle Maddix and Yuyang Wang.

    Machine Learning Research Intern

    2019.05 - 2019.08
    Berkeley Lab, Berkeley, CA; supervised by Karthik Kashinath, Mustafa Mustafa, and Adrian Albert.

    Data Science Co-op

    2018.07 - 2018.12
    Abiomed Inc., Danvers, MA; supervised by Chen Liu and Erik Kroeker.