Shaocong Ma's CV
Education
- Ph.D. in Electrical and Computer Engineering
University of Utah
2019.8 - 2024.5 - M.A. in Statistics
University of California, Santa Barbara
2017.9 - 2019.6 - B.S. in Statistics
Sichuan University
2013.9 - 2017.6
Research Experiences
Postdoctoral Researcher
University of Maryland
2024.6 - Present
Supervisor: Professor Heng Huang
The duties involve developing more efficient and robust machine learning systems, leading and mentoring several large-scale machine learning research projects, and publishing results in top-tier conferences such as ICLR.Research Intern
Lawrence Livermore National Security
2022.5 - 2022.8
Supervisor: Bhavya Kailkhura, James Diffenderfer
The duties included designing a hybrid model incorporating external black-box PDE solvers to tackle non-differentiability challenges in fluid flow predictions and rigorously evaluating the resilience of Physics-Informed Graph Neural Networks in out-of-distribution scenarios, achieving performance on par with differentiable solvers.Research Assistant
University of Utah
2019.8 - 2024.5
Supervisor: Professor Yi Zhou
The duties included leading machine learning projects in Professor Yi Zhou’s lab, designing fast and stable algorithms for large-scale machine learning and reinforcement learning, and publishing results in top conferences such as ICML, NeurIPS, and ICLR, as well as top journals like JMLR.
Publications
- Deep learning of PDE Correction and Mesh Adaption without Automatic Differentiation
Machine Learning, 2025.
Shaocong Ma, James Diffenderfer, Bhavya Kailkhura, and Yi Zhou. - Revisiting Zeroth-Order Optimization: Minimum-Variance Two-Point Estimators and Directionally Aligned Perturbations
ICLR, 2025. ★ Spotlight
Shaocong Ma, Heng Huang. - Stochastic Optimization Methods for Policy Evaluation in Reinforcement Learning
Foundations and Trends® in Optimization, 2024.
Yi Zhou, Shaocong Ma. - Decentralized Robust V-Learning for Solving Markov Games with Model Uncertainty
JMLR, 2023.
Shaocong Ma, Ziyi Chen, Shaofeng Zou, Yi Zhou. - End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solver
NeurIPS, 2023 (ML4PS Workshop).
Shaocong Ma, James Diffenderfer, Bhavya Kailkhura, and Yi Zhou. - Finding Correlated Equilibrium of Constrained Markov Game: A Primal-Dual Approach
NeurIPS, 2022.
Ziyi Chen, Shaocong Ma, Yi Zhou. - Data Sampling Affects the Complexity of Online SGD over Dependent Data
UAI, 2022.
Shaocong Ma, Ziyi Chen, Yi Zhou, Kaiyi Ji, Yingbin Liang. - Accelerated Proximal Alternating Gradient-Descent-Ascent for Nonconvex Minimax Machine Learning
IEEE ISIT, 2022.
Ziyi Chen, Shaocong Ma, Yi Zhou. - Sample Efficient Stochastic Policy Extragradient Algorithm for Zero-Sum Markov Game
ICLR, 2022.
Ziyi Chen, Shaocong Ma, Yi Zhou. - Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity
ICLR, 2021.
Shaocong Ma, Ziyi Chen, Yi Zhou, Shaofeng Zou. - Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis
NeurIPS, 2020.
Shaocong Ma, Yi Zhou, Shaofeng Zou. - Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle
ICML, 2020.
Shaocong Ma, Yi Zhou.
Teaching
- Teaching Assistant (University of Utah)
University of Utah
2020 - 2021
Instructed the lab section, assisted with course materials, and graded assignments. Courses include:- ECE 3500: Fundamentals of Signals and Systems
- Teaching Assistant (University of California, Santa Barbara)
University of California, Santa Barbara
2018 - 2019
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
Professional Services
- Conference Reviewer/Program Committee
- International Conference on Machine Learning (ICML)
- International Conference on Learning Representations (ICLR)
- Advances in Neural Information Processing Systems (NeurIPS)
- IEEE International Conference on Big Data (IEEE BigData)
- International Joint Conference on Artificial Intelligence (IJCAI)
- Conference on Uncertainty in Artificial Intelligence (UAI)
- AAAI Conference on Artificial Intelligence (AAAI)
- International Conference on Artificial Intelligence and Statistics (AISTATS)
- Reinforcement Learning Conference (RLC)
- Journal Reviewer
- Transactions on Machine Learning Research (TMLR)
- IEEE Transactions on Signal Processing
- Numerical Algorithms
- IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI)
- European Journal of Control
- Workshop Reviewer
- ICLR 2024 Blogpost
Skills
- Machine Learning & Deep Learning
- Large Language Models (LLMs)
- Reinforcement Learning
- Graph Neural Networks
- Classical Machine Learning & Statistical Methods s
- Optimization & Numerical Methods
- PDE-Based Modeling
- Hybrid Differentiable & Black-Box Solvers
- Large-Scale Optimization
- Algorithmic Efficiency & Robustness
- Out-of-Distribution Analysis
- High-Performance Computing & Software Engineering
- Parallel & Distributed Computing
- GPU Acceleration
- Version Control & DevOps (Git, CI/CD)
- Linux Environments & Shell Scripting
- Programming & Tools
- Languages: Python (NumPy, SciPy, PyTorch, TensorFlow, scikit-learn), R, MATLAB
- Data Analysis & Visualization: Pandas, Matplotlib, Seaborn, Plotly
- LaTeX & Document Preparation: Writing and formatting research papers, technical documents, and presentations
- Research & Leadership
- Project Management & Mentorship
- Academic Publishing & Reviewing
- Scientific Communication & Presentations
- Collaborations & Cross-Disciplinary Work