Ph.D. Candidate
Computer Science, Machine Learning

I am a Ph.D. candidate (funded by the A*STAR ACIS Scholarship) in the department of computer science at National University of Singapore (NUS) where I study "Fair Data Valuation in Collaborative Machine Learning". My advisors are Bryan Kian Hsiang Low and Chuan-Sheng Foo. My research interests include data-centric machine learning, multi-agent machine learning systems, data sharing and incentive mechanisms. Prior to my Ph.D., I received my Bachelor degrees in computing and applied mathematics (a double-degree programme) from NUS in 2019; I have also spent time as an algorithm developer intern at Ant Financial (Hangzhou, China) and Sensetime (Singapore).

In 2022-23, I was fortunate to spend time at CyLab in ECE of Carnegie Mellon University, advised by Giulia Fanti and supported by A*STAR.

[Currently based in Berkeley, CA, U.S.] I am visiting UC Berkeley, hosted and advised by Michael I. Jordan. Ping me for a chat or coffee if you are in the bay area!

Research Experience

• 2024/2 - present: University of California, Berkeley (UCB), EECS.
  Visiting Scholar hosted and advised by Michael I. Jordan.
• 2022/8 - 2023/4: Carnegie Mellon University (CMU), ECE.
  Visiting Scholar hosted and advised by Giulia Fanti.
• 2019/8 - 2020/7: National University of Singapore (NUS), Dept. of CS.
  Research assistant advised by Leong Tze-Yun.

Education

• 2020 - present: National University of Singapore (NUS), Dept. of CS.
  Ph.D. in Computer Science, supported by ACIS scholarship.,
• 2015 - 2019: National University of Singapore (NUS), Dept. of CS.
  B.Comp. in Computing, supported by Science and Technology Scholarship, Highest distinction with Honours, Turing Programme. Double-degree Programme.
• 2015 - 2019: National University of Singapore (NUS), Faculty of Science.
  B.Sci. in Applied Mathematics, supported by Science and Technology Scholarship. Double-degree Programme.

Recent news

• 2024/3 - I gave a talk at Federated Learning One World Seminar (FLOW) on Fairness and incentives in data sharing & collaborative learning. [Slides]
  
• 2024/3 - The Federated Learning: Theory and Practice with our contributed chapters (Chapter 8 on fairness, Chapter 15 on data valuation, Chapter 16 on incentives) is officially out! These chapters serve as great introductory materials for anyone interested in these topics in federated learning.
  
• 2024/2 - Starting as a visiting scholar at EECS of University of California, Berkeley to work on incentive mechanisms for data sharing, hosted and advised by Michael I. Jordan.
  
• 2024/2 - Invited to serve as a reviewer for ICML 2024 workshop proposal.
  
• 2024/1 - I received the Research Achievement Award in semster 1 of the academic year 2023-2024.
  
• 2023/12 - Invited to serve as a reviewer for ICML 2024, IJCAI 2024 (main track and survey track).
  
• 2023/11 - I successfully proposed my Ph.D. thesis: Data Valuation for Fair Collaborative Machine Learning.
  
• 2023/11 - I received Top Reviewer for NeurIPS 2023.
  
• 2023/9 - Two of our papers accepted to NeurIPS 2023.
  
• 2023/9 - Invited to serve as a reviewer for AAAI 2024 and AAMAS 2024.
  
• 2023/8 - Invited to serve as a reviewer for AISTATS 2024 and ICLR 2024.
  
• 2023/6 - Selected for the Teaching Fellowship Scheme (July 2023 - June 2024) by School of Computing, NUS as 1 of 5 CS Ph.D. students for excellent past performance as a tutor.
  
• 2023/4 - Two of our papers accepted to ICML 2023.
  
• 2023/4 - Invited to serve as a reviewer for NeurIPS 2023.
  
• 2023/2 - I received Top Reviewer (Top 10%) for AISTATS 2023.
  
• 2023/1 - Our paper FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery is accepted to AISTATS 2023.
  
• 2023/1 - Our AAAI2023 paper Probably Approximate Shapley Fairness with Applications in Machine Learning received Oral presentation.
  
• 2022/12 - Invited to serve as a reviewer for ICML 2023.
  
• 2022/11 - Our paper Probably Approximate Shapley Fairness with Applications in Machine Learning is accepted to AAAI 2023.
  
• 2022/11 - Invited to give a talk at the 2022 Workshop on Federated Learning and Analytics by Google on our NeurIPS'21 work on Collaborative machine learning/Federated learning [Slides].
  
• 2022/10 - I received Top Reviewer for NeurIPS 2022.
  
• 2022/9 - Invited to serve as a PC member (reviewer) for AISTATS 2023.
  
• 2022/8 - Nominated as 1 of 5 Ph.D. students by School of Computing, NUS for Apple Scholars in AI/ML 2023 Ph.D. Fellowship.
  
• 2022/8 - Invited to serve as a PC member (reviewer) for ICLR 2023 and AAMAS 2023.
  
• 2022/8 - Starting as a visiting scholar (at the ECE department) of Carnegie Mellon University, hosted and advised by Giulia Fanti to work on data sharing and federated learning.
  
• 2022/7 - I received Outstanding Reviewer (Top 10%) for ICML 2022.
  
• 2022/6 - I gave a talk at Google about Collaborative machine learning/Federated learning. [Slides]
  
2022/4 - Our paper On the Convergence of the Shapley Value in Parametric Bayesian Learning Games is accepted to ICML 2022.

2022/4 - Our survey paper Data Valuation in Machine Learning: "Ingredients", Strategies, and Open Challenges is accepted to IJCAI-ECAI2022 Survey Track.

2022/3 - Invited to serve as a reviewer for NeurIPS 2022.

2022/2 - I am placed on the Honor list of student tutors for the academic year 2020-2021 (rated: 4.58/5 by students) for when I taught CS3244 Machine learning as a TA for Bryan Kian Hsiang Low.

2022/1 - Invited to serve as a reviewer for ICML 2022.

2022/1 - I received the Research Achievement Award in semster 1 of the academic year 2021-2022.

2021/12 - Our paper Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards is accepted as an Oral presentation to AAAI 2022.

2021/9 - Our paper Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning is accepted to NeurIPS 2021.

2021/9 - Our paper Validation Free and Replication Robust Volume-based Data Valuation is accepted to NeurIPS 2021.

2021/7 - Invited to serve as a PC member (reviewer) for the workshop New Frontier in Federated Learning at NeurIPS'21.

2021/7 - Our paper A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning received an Oral presentation at FL-ICML'21.

2020/12 - Our paper Collaborative Fairness in Federated Learning received the Best Paper Award at FL-IJCAI2020 and is accepted as a book chapter in Federated Learning.

2020/08 - I started my Ph.D.
National Unversity of Singapore

2020/08 - Our paper Hierarchical Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection is accepted to ICAPSS-PRL2020.

2020/01 - I started working as a research assistant under the direction of LEONG, Tze-Yun on decision-making under uncertainty.
Medical Computing Lab, National Unversity of Singapore
more