About Me

I am a computer science researcher specializing in machine learning, with a strong emphasis on methodology and problem-solving processes rather than focusing on any one specific problem. My research spans theory, algorithms, and systems for machine learning, and I also occasionally explore topics in computational sociology and economics. I completed my undergraduate studies at the University of British Columbia, where I studied computer engineering and pure mathematics. I then pursued a Master’s degree at the University of Toronto, researching computational social networks under the supervision of Prof. Peter Marbach. I earned my PhD from the University of Hong Kong, where I have had the privilege of working with Prof. Chuan Wu (highly recommended for those interested in large-scale machine learning algorithms and systems) and Prof. Difan Zou (highly recommended for those interested in the fundamental aspects of machine learning). Please see my CV or my google scholar for the publication records.

I will be joining the University of Science and Technology of China (USTC) as an associate professor by the end of 2025. I am currently seeking research assistants with a strong background in mathematics and programming to collaborate on projects at the intersection of machine learning theory, algorithms, and systems. Successful candidates may have the opportunity to transition into a PhD position at USTC or receive recommendations and connections to other internationally reputed institutions. If you are interested in working with me, please email a brief introduction, your background, and the research areas or projects you are interested in pursuing.

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News

• will serve as an Area Chair for ICLR 2026
• one paper (as corresponding author) is accepted in VLDB 2025
• three papers (as first author) are accepted in ICML 2025

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Education

• Doctor of Philosophy (Computer Science)
  University of Hong Kong
  Hong Kong PhD Fellowship Scheme (HKPFS)
  University of Hong Kong Presidential Scholar

• Master of Science (Computer Science)
  University of Toronto

• Bachelor of Applied Science (Computer Engineering and Pure Mathematics)
  University of British Columbia

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Working Projects

1. Trustworthy and Efficient Graph Learning
   Focused on developing scalable and reliable graph learning algorithms that prioritize both performance and interpretability, with applications in large-scale networks and real-time systems.

2. Second-Generation Machine Learning Systems: Co-Design of Theory, Algorithms, and Systems
   A project aimed at creating the next generation of machine learning systems by integrating theoretical advancements with practical system-level design, optimizing both algorithmic efficiency and system architecture.

3. AI for Science and Operational Research
   Exploring the intersection of artificial intelligence and scientific/operational research, this project focuses on applying machine learning methods to solve complex scientific/operational problems, ranging from computational physics to biology and environmental science.

4. Data-Efficient Machine Learning
   Focusing on developing machine learning models that require fewer data samples while maintaining high performance, with a focus on improving generalization, reducing training costs and improving the data curation process.

5. Multi-Agent System
   This project seeks to better understand the dynamics of multi-agent systems (MAS) and to develop mechanisms and learning algorithms that enhance the performance of MAS.

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Professional Services

Actively serve as AC/Reviewers/Editors for the following venues:

• Conference: ICML, Neurips, ICLR, AISTAT, KDD, AAAI

• Journal: ToN, TKDE, TKDD, TNNLS