Ph.D. Student
Computer Science, Machine Learning

I am a third year Ph.D. student (funded by A*STAR through the ACIS Scholarship) in the department of computer science at National University of Singapore where I study multi-agent machine learning systems. My advisors are Bryan Kian Hsiang Low and Chuan-Sheng Foo. I was fortunate to spend time at CyLab in ECE of Carnegie Mellon University where I was advised by Giulia Fanti. My research interests include theoretical and foundational machine learning, multi-agent systems, collaborative machine learning/federated learning, reinforcement learning.

I received my Bachelor degrees in computing and applied mathematics (a double-degree programme) from NUS in 2019. I studied machine learning under the direction of Bryan Kian Hsiang Low. There I worked on applying game theory (i.e., Nash equilibrium) to multi-party machine learning for my dissertation.

Education

• Ph.D. in Computer Science, 2020 - present
  National Unversity of Singapore, ACIS Scholarship
• B.Comp in Computing and B.Sc in Applied Mathemetics, 2015 - 2019
  National Unversity of Singapore, Science and Technology Scholarship, Highest distinction with Honours, Turing Programme, Double-degree Programme

Recent news

• 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.
  
• 2022/11 - 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 - Invited to serve as a PC member (reviewer) for ICLR 2023 and AAMAS 2023.
  
• 2022/8 - Arrived at Carnegie Mellon University as a visiting scholar (at the ECE department), hosted 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
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