Biography

Shaocong Ma is a fifth Ph.D. student at University of Utah, supervised by Yi Zhou. Before, Shaocong earned an M.A. degree in Statistics at University of California, Santa Barbara, and a B.S. degree in Statistics at Sichuan University.

My most-updated CV: PDF

Research summary: PDF

Research Interests:

  • Optimization and Reinforcement Learning: Driven by real-world challenges, my research is centered on optimization techniques applied in diverse fields such as (multi-agent) reinforcement learning and physic-informed machine learning. More specifically, I prioritize creating data-efficient and environment-robust algorithms that are substantiated with theoretical guarantees.

Professional Services:

  • Conference Reviewer/Program Committee: ICML; ICLR; NeurIPS; IEEE BigData; IJCAI; UAI; AAAI; AISTAT.
  • Journal Reviewer: Transactions on Machine Learning Research (TMLR); IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI); European Journal of Control.
  • Workshop Reviewer: ICLR 2024 Blogpost.

Experiences

Student Intern (AI4Science)

2022.5 - 2022.8
Lawrence Livermore National Security

(1) Designed a hybrid model with external black-box PDE solvers, addressing the non-differentiability challenges in fluid flow predictions. (2) Rigorously assessed the Physics-Informed Graph Neural Network’s resilience in out-of-distribution scenarios, achieving comparable performance with differentiable solvers.

Research Collaborator (AI4Science)

2022.8 - Present
Lawrence Livermore National Security

(1) Integrated a MAML-type meta-learning model with a non-differentiable external PDE solver, enhancing fluid flow prediction capabilities. (2) Led and managed experiments progress using Git and conducted comprehensive analysis of results.

Research Assistant

2019 - Present
University of Utah

I led several machine learning projects in Professor Yi Zhou’s lab. My role is to design fast and stable algorithms in the large-scale machine learning and reinforcement learning. Results developed during this period were published on top conferences including ICML, NeurIPS, and ICLR.

Teaching Assistant

2020 - 2021
University of Utah

I instructed the lab section of an electrical and computer engineering course during the Ph.D. program. Course title:

  • ECE 3500: Fundamentals of Signals and Systems

Teaching Assistant

2018 - 2019
University of California, Santa Barbara

I instructed sections/labs of statistics and data science courses during the Master program. Courses include:

  • PSTAT 5A: Statistics
  • PSTAT 5LS: Statistics for Life Science
  • PSTAT 109: Statistics for Economics
  • PSTAT 172A: Actuarial Statistics
  • PSTAT 175: Survival Analysis

Publications

  • Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis
  • Shaocong Ma, Yi Zhou, Shaofeng Zou
    NeurIPS. 2020. (Acceptance rate: 20.1%)
  • Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity
  • Shaocong Ma, Ziyi Chen, Yi Zhou, Shaofeng Zou
    ICLR. 2021. (Acceptance rate: 28.7%)
  • Sample Efficient Stochastic Policy Extragradient Algorithm for Zero-Sum Markov Game
  • Ziyi Chen, Shaocong Ma, Yi Zhou
    ICLR. 2022. (Acceptance rate: 32.3%)
  • Data Sampling Affects the Complexity of Online SGD over Dependent Data
  • Shaocong Ma, Ziyi Chen, Yi Zhou, Kaiyi Ji, Yingbin Liang
    UAI. 2022.
  • Finding Correlated Equilibrium of Constrained Markov Game: A Primal-Dual Approach
  • Ziyi Chen, Shaocong Ma, Yi Zhou
    NeurIPS. 2022. (Acceptance rate: 25.6% )
  • Decentralized Robust V-Learning for Solving Markov Games with Model Uncertainty.
  • Shaocong Ma, Ziyi Chen, Shaofeng Zou, Yi Zhou
    JMLR 2023.
  • End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solver.
  • Shaocong Ma, James Diffenderfer, Bhavya Kailkhura, and Yi Zhou
    NeurIPS 2023 (ML4PS Workshop).
  • When Non-Differentiable PDE Solver Meets Deep Learning: Partially Differentiable Learning for Efficient Fluid Flow Prediction
  • Shaocong Ma, James Diffenderfer, Bhavya Kailkhura, and Yi Zhou
    Submitted.

    Others

    Session/Lab Notes, Presentation Slides, and Course Projects.