Ph.D. Student
Computer Science, Machine Learning
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National University of Singapore School of Computing, 13, Computing Dr. Singapore 117417 |
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| Google scholar | |
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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. 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 2020. 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.
Recent update: I am currently in Pittsburgh visiting CMU, till April 2023. Happy to grab a coffee and chat about research if you are also in the area, or if you have suggestions on where I should visit!
Education
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Ph.D. in Computer Science, 2020 - present
National Unversity of Singapore, ACIS Scholarship -
B.Comp in Computing and B.Sc in Applied Mathemetics, 2015 - 2020
National Unversity of Singapore, Science and Technology Scholarship, Highest distinction with Honours, Turing Programme, Double-degree Programme
Recent news
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2023/2 - I received Top Reviewer (Top 10%) for AISTATS 2023.
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2023/1 - Our paper FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery is accepted to AISTATS 2023.
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2023/1 - Our AAAI2023 paper Probably Approximate Shapley Fairness with Applications in Machine Learning received Oral presentation.
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2022/11 - Our paper Probably Approximate Shapley Fairness with Applications in Machine Learning is accepted to AAAI 2023.
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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 ].
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2022/10 - I received Top Reviewer for NeurIPS 2022.
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2022/9 - Invited to serve as a PC member (reviewer) for AISTATS 2023.
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2022/8 - Invited to serve as a PC member (reviewer) for ICLR 2023 and AAMAS 2023.
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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.
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2022/7 - I received Outstanding Reviewer (Top 10%) for ICML 2022.
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2022/6 - I gave a talk at Google about Collaborative machine learning/Federated learning. [
Slides ]
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2022/4 - Our paper On the Convergence of the Shapley Value in Parametric Bayesian Learning Games is accepted to ICML 2022.
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2022/4 - Our survey paper Data Valuation in Machine Learning: "Ingredients", Strategies, and Open Challenges is accepted to IJCAI-ECAI2022 Survey Track.
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2021/3 - Invited to serve as a reviewer for NeurIPS 2022.
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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.
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2021/1 - Invited to serve as a reviewer for ICML 2022.
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2022/1 - I received the Research Achievement Award in semster 1 of the academic year 2021-2022.
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2021/12 - Our paper Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards is accepted as an Oral presentation to AAAI 2022.
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2021/9 - Our paper Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning is accepted to NeurIPS 2021.
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2021/9 - Our paper Validation Free and Replication Robust Volume-based Data Valuation is accepted to NeurIPS 2021.
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2021/7 - Invited to serve as a PC member (reviewer) for the workshop New Frontier in Federated Learning at NeurIPS'21.
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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.
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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.
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2020/08 - I started my Ph.D.
National Unversity of Singapore
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2020/08 - Our paper Hierarchical Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection is accepted to ICAPSS-PRL2020.
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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