PhD Candidate · Northeastern University
I am a PhD candidate in Computer Engineering at Northeastern University, advised by Prof. Lili Su. My research focuses on federated reinforcement learning, particularly theoretical convergence guarantees for federated RL algorithms — such as Fed-Q-Learning and Fed-Actor-Critic — under heterogeneous environments.
Before Northeastern, I received an MSc in Statistics and Operational Research from the University of Edinburgh (with Distinction), working with Prof. Daniel Paulin, and a Bachelor of Computer Science from the University of Waterloo.
Research Interests
- Federated Reinforcement Learning
- Reinforcement Learning
- Theoretical Guarantee
- Heterogeneous Environments
- Personalized Policy Training
- Exoskeleton
Publications
Accepted
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On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments
Transactions on Machine Learning Research (TMLR)
Workshop version: International Workshop on Federated Foundation Models (FL@FM), NeurIPS 2024
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On the Power of Source Screening for Learning Shared Feature Extractors
International Conference on Machine Learning (ICML) Spotlight
Under Review
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Collaborative Yet Personalized Policy Training: Single-Timescale Federated Actor-Critic
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Personalized Multi-Agent Average Reward TD-Learning via Joint Linear Approximation
Education
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2023 – Present
Northeastern University, Boston, MA, USA
PhD Candidate in Computer Engineering · Advisor: Prof. Lili Su
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2021 – 2022
University of Edinburgh, Edinburgh, UK
MSc in Statistics and Operational Research, with Distinction · Advisor: Prof. Daniel Paulin
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2015 – 2020
University of Waterloo, Waterloo, Canada
Bachelor of Computer Science
Academic Services
Reviewer: ICML ARLET Workshop 2024; ICML 2026.
