Biography
Shaocong Ma is a postdoctoral researcher at the University of Maryland, working under the supervision of Professor Heng Huang. He received his Ph.D. in Electrical and Computer Engineering from the University of Utah, where he was advised by Professor Yi Zhou. Previously, he completed an M.A. in Statistics at the University of California, Santa Barbara, and earned his B.S. in Statistics from 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
I joined the Heng Huang lab as a postdoctoral researcher. I led several Large Language Model (LLM) projects and contributed to more efficient and robust machine learning systems.
(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.
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.