PhD Candidate

Educational Experience:

• Master Degree in Mathematics, Courant Institue of Mathematical Sciences, New York University
• Bachelor Degree in Data Science with a concentration in Internet Finance, School of EECS, Peking University

Research Interests:

• Multi-agent Games on Mathematical Structures (Graphs & Graphons)
• Multi-agent Games with Adversarial Interactions (Deception & Counter-Deception)
• Deep & Reinforcement Learning (Architectures & Algorithms) for Solving Games
• Optimal Transport and its Applications in Generative Modeling
• Signatures and its Applications in Machine Learning
• Developments and analysis of mathematical models for finance, biology, and social science

Publications (code available on GitHub):

1. Deception in Linear-Quadratic Control, joint work with Y. Kim, A. Benvenuti, R. Hu, M. Hale
   Submitted
2. Strategic Inference in Stackelberg Games: Optimal Control for Revealing Adversary Intent, joint work with D. Ralston, X. Yang, R. Hu
   Submitted
3. Learning Mean-Field Games through Mean-Field Actor-Critic Flow, joint work with M. Zhou, R. Hu
   Submitted
4. Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures, joint work with R. Hu, J. Long
   Submitted
5. Adversarial Decision-Making in Partially Observable Multi-Agent Systems: A Sequential Hypothesis Testing Approach, joint work with D. Ralston, X. Yang, R. Hu
   Accepted by IEEE Transactions on Control of Network Systems (TCNS)
6. Integrating Sequential Hypothesis Testing into Adversarial Games: A Sun Zi-Inspired Framework, joint work with D. Ralston, X. Yang, R. Hu
   In Proceedings of IEEE Conference on Decision and Control (CDC) 2025, DOI: 10.1109/CDC57313.2025.11312291 [arXiv] [Conference]
7. Finite-Agent Stochastic Differential Games on Large Graphs: I. The Linear-Quadratic Case, joint work with R. Hu, J. Long
   Applied Mathematics & Optimization, DOI: 10.1007/s00245-025-10309-8 [arXiv] [Journal]
8. Detecting Boolean Asymmetric Relationships with a Loop Counting Technique and its Implications for Analyzing Heterogeneity within Gene Expression Datasets, H. Zhou, W. Lin, S.R. Labra, S.A. Lipton, J.A. Elman, N.J. Schork, A.V. Rangan
   IEEE/ACM Transactions on Computational Biology and Bioinformatics, DOI: 10.1109/TCBB.2024.3487434 [bioRxiv] [Journal]
9. Approximation Algorithms and PTAS of the Minimum Dominating Set and the 3-Path Vertex Cover Problem on Unit Disk Graphs, H. Zhou, J. Tu, Y. Kong
   Bachelor Thesis, not to be published

Teaching Experience:

• TA for UCSB PSTAT 213C: Graduate Level Stochastic Process (Spring 2025)
• TA for UCSB PSTAT 213B: Graduate Level Stochastic Process (Winter 2025)
• TA for UCSB PSTAT 213A: Graduate Level Stochastic Process (Fall 2024)
• TA for UCSB PSTAT 213C: Graduate Level Stochastic Process (Spring 2024)
• TA for UCSB PSTAT 213B: Graduate Level Stochastic Process (Winter 2024)
• TA for UCSB PSTAT 213A: Graduate Level Stochastic Process (Fall 2023)
• TA for UCSB PSTAT 160A: Stochastic Process (Summer 2023)
• TA for UCSB PSTAT 120A: Probability and Statistics (Spring 2023)
• TA for UCSB PSTAT 8: Transition to Data Science, Probability and Statistics (Winter 2023)
• TA for UCSB PSTAT 120B: Probability and Statistics (Winter 2023)
• TA for UCSB PSTAT 170: Introduction to Mathematical Finance (Fall 2022)
• TA for NYU MATH-UA 009: Algebra and Calculus (Spring 2022)

Other Resources:

• RSS Feed for arXiv Preprints
• Notes and Codes
• A website for particle system simulation, constructed with L.T. Rolla, X. Ma, and Y. Dai
• Some maths notes in Chinese posted on Zhihu