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