I am a PhD Candidate at UC San Diego department of Computer Science and Engineering advised by Prof. Rose Yu and a research intern at Google Cloud AI. 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 interests are in spatiotemporal learning and physics-guided deep learning. For more details, see my resume and google scholar.

I am on job market this year, looking for research positions in the industry.

Selected Publications

  • 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 - Present
    Google Cloud AI, Sunnyvale, CA; supervised by Yihe Dong and Sercan O.Arik.
    • Investigated deep leaning approaches for forecasting highly nonstationary time series.
    • Proposed KNF that achieves SOTA performance on many financial times series datasets.

    Machine Learning Research Intern

    2021.06 - 2021.09
    Lawrence Livermore National Laboratory, Livermore, CA; supervised by Brenda Ng.
    • Developed neural network surrogate models to inform carbon capture design.
    • Implemented mesh-based graph neural networks for CFD simulation surrogate development.

    Applied Scientist Intern

    2020.05 - 2020.08
    Amazon Web Services, Palo Alto, CA; supervised by Danielle Maddix and Yuyang Wang.
    • Developed an ODEs-based method for COVID-19 trajectories forecasting.
    • Researched the generalizability of deep learning models on learning non-linear dynamical systems.

    Machine Learning Research Intern

    2019.05 - 2019.08
    Berkeley Lab, Berkeley, CA; supervised by Karthik Kashinath, Mustafa Mustafa, and Adrian Albert.
    • Researched the challenging task of spatiotemporal modeling of nonlinear turbulent flows.
    • Developed TF-net that unifies RANS-LES coupling with custom-designed U-net.

    Research Assistant

    2019.01 - 2019.04
    Northeastern University, Boston, MA; supervised by Prof. Rose Yu.
    • Studied long-term forecast of patients’ Aortic Pressure with deep sequence learning.

    Data Science Co-op

    2018.07 - 2018.12
    Abiomed Inc., Danvers, MA; supervised by Chen Liu and Erik Kroeker.
    • Applied deep sequence models innovatively to the estimation of left ventricular volume.
    • Created Healthy Heart Index based on SVM for predicting patients’ survival probabilities.

    Research Assistant

    2018.04 - 2018.11
    Northeastern University, Boston, MA; supervised by Prof. Mitchell Wand.
    • Proposed a statistical method based on Beta-Binomial model for systematic teacher evaluation.

    Invited Talks

    M2LInES project team - Columbia University (07/2022)
    AI Research Seminar - UC San Diego (02/2022)
    Scripps Institution of Oceanography - UC San Diego (11/2021)
    Artificial Intelligence in Astronomy Group - University Of São Paulo (11/2021)
    Scientific Machine Learning Webinar Series - Carnegie Mellon University (10/2021)
    Data Driven Physics Simulation Seminar - Lawrence Livermore National Laboratory (07/2021)
    Data Science and Machine Learning Seminar - Agency for Science, Technology and Research (06/2021)
    Spatiotemporal Reading Group - National Energy Research Scientific Computing Center (05/2020)

    Skills & Proficiency

    Python & Pytorch & Jax & R

    Tensor Flow & Keras

    C & C++ & C# & SQL

    Matlab & JavaScript