Zhongxiang Dai/代忠祥

I'm a 4th-year Ph.D. candidate in Department of Computer Science, National University of Singapore, working on AI/machine learning. I'm interested in sequential decision-making under uncertainty (e.g., Bayesian optimization, reinforcement learning, multi-armed bandit and active learning), and automated machine learning. Previously, I also worked on computational neuroscience.

Education

• National University of Singapore (NUS)   (Aug 2017 - present)
  • Ph.D. student in Artificial Intelligence, Department of Computer Science
  • Advisors: Bryan Kian Hsiang Low (NUS) & Patrick Jaillet (MIT)
  • Supported by Singapore-MIT Alliance for Research and Technology (SMART) Graduate Fellowship, eligible for co-supervision by an MIT faculty and research residency at MIT for up to six months
• National University of Singapore (NUS)   (Aug 2011 - Jun 2015)
  • Bachelor of Engineering (Electrical Engineering), First Class Honors

Publications

2020

• Federated Bayesian Optimization via Thompson Sampling.
  Zhongxiang Dai, Kian Hsiang Low and Patrick Jaillet.
  In 34th Conference on Neural Information Processing Systems (NeurIPS-20), Dec 6-12, 2020.
  Acceptance rate: 20.1%. [Code]
  Key Words: Bayesian optimization, federated learning, Thompson sampling.
• R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games.
  Zhongxiang Dai, Yizhou Chen, Kian Hsiang Low, Patrick Jaillet and Teck-Hua Ho.
  In 37th International Conference on Machine Learning (ICML-20), Jul 12-18, 2020.
  Acceptance rate: 21.8%. [Code, Proceedings, Video]
  Key Words: Bayesian optimization, adversarial machine learning, multi-agent reinforcement learning, game theory.
• Private Outsourced Bayesian Optimization.
  Dmitrii Kharkovskii, Zhongxiang Dai and Kian Hsiang Low.
  In 37th International Conference on Machine Learning (ICML-20), Jul 12-18, 2020.
  Acceptance rate: 21.8%. [Code, Proceedings]
  Key Words: Bayesian optimization, differential privacy.

2019

• Bayesian Optimization Meets Bayesian Optimal Stopping.
  Zhongxiang Dai, Haibin Yu, Kian Hsiang Low, and Patrick Jaillet.
  In 36th International Conference on Machine Learning (ICML-19), Long Beach, CA, Jun 9-15, 2019.
  Acceptance rate: 22.6%. [Code, Proceedings]
  Key Words: Bayesian optimization, early stopping, multi-fidelity, reinforcement learning, feature selection.
• Bayesian Optimization with Binary Auxiliary Information.
  Yehong Zhang, Zhongxiang Dai, and Kian Hsiang Low.
  In Conference on Uncertainty in Artificial Intelligence (UAI-19) , Tel Aviv, Israel, Jul 22-25, 2019.
  Acceptance rate: 26.2% (plenary talk). [Code]
  Key Words: Bayesian optimization, multi-fidelity, entropy search, reinforcement learning.
• Implicit Posterior Variational Inference for Deep Gaussian Processes.
  Haibin Yu*, Yizhou Chen*, Zhongxiang Dai, Kian Hsiang Low, and Patrick Jaillet.
  In 33th Conference on Neural Information Processing Systems (NeurIPS-19). Vancouver, Canada, Dec 7 - 12, 2019.
  Acceptance rate: 3% (spotlight). [Code]
  Key Words: deep Gaussian processes, generative adversarial networks, game theory.

Previous Publications on Computational Neuroscience

• Stress-induced Changes in Modular Organizations of Human Brain Functional Networks.
  Yuan Zhang, Zhongxiang Dai, Jianping Hu, Shaozheng Qin, Rongjun Yu, and Yu Sun.
  Neurobiology of Stress, 2020.
• Altered Intra and Inter-hemispheric Functional Dysconnectivity in Schizophrenia.
  Yuan Zhang, Zhongxiang Dai, Yu Chen, Kang Sim, Yu Sun, and Rongjun Yu.
  Brain Imaging and Behavior, 2018.
• Functional Connectivity Analysis of Mental Fatigue Reveals Different Network Topological Alterations Between Driving and Vigilance Tasks.
  Georgios N. Dimitrakopoulos, Ioannis Kakkos, Zhongxiang Dai, Hongtao Wang, Kyriakos Sgarbas, Nitish Thakor, Anastasios Bezerianos, and Yu Sun
  IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018
• Modular Level Alterations of Structure Function Coupling in Schizophrenia Connectome.
  Yu Sun, Zhongxiang Dai, Junhua Li, Simon L. Collinson, and Kang Sim.
  Human Brain Mapping, 2017.
• The Effects of A Mid-task Break on the Brain Connectome in Healthy Participants: A Resting-state Functional MRI Study.
  Yu Sun*, Julian Lim*, Zhongxiang Dai, KianFoong Wong, Fumihiko Taya, Yu Chen, Junhua Li, Nitish Thakor, and Anastasios Bezerianos.
  NeuroImage, 2017.
• EEG Cortical Connectivity Analysis of Working Memory Reveals Topological Reorganization in Theta and Alpha Bands.
  Zhongxiang Dai, Joshua De Souza, Julian Lim, Paul M. Ho, Yu Chen, Junhua Li, Nitish Thakor, Anastasios Bezerianos, and Yu Sun.
  Frontiers in Human Neuroscience, 2017.
• Task-independent Mental Workload Classification Based upon Common Multiband EEG Cortical Connectivity.
  Georgios N. Dimitrakopoulos*, Ioannis Kakkos*, Zhongxiang Dai, Julian Lim, Anastasios Bezerianos, and Yu Sun.
  IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017
• Temporal Efficiency Evaluation and Small-worldness Characterization in Temporal Networks.
  Zhongxiang Dai, Yu Chen, Junhua Li, Johnson Fam, Anastasios Bezerianos, and Yu Sun.
  Scientific Reports, 2016.

Awards and Honors

• Singapore-MIT Alliance for Research and Technology (SMART) Graduate Fellowship, Aug 2017
• JDDiscovery Population Dynamics Census and Prediction Competition 2018 (annual competition hosted by JD.com): global champion, ranked 1st among > 2,100 teams, Jan 2019 (News in English, News in Chinese, pictures taken before I started my diet :) refer to the profile pic for what I look like now)
• Research Achievement Award × 2, NUS, School of Computing, 2019 & 2020
• ST Electronics Prize × 2 (the top student in the cohort of Electrical Engineering Year 1 & 2, NUS), Academic Year 2011/2012 & 2012/2013
• Dean’s List × 5 (top 5% in Electrical Engineering, NUS), 2011-2015
• Selected founding member of IEEE-Eta Kappa Nu (HKN) society, NUS Chapter, Mar 2013
• Singapore Ministry of Education SM3 scholarship for undergraduate PRC students, 2010

Professional Services

• Program committee member for CoRL 2020, NeurIPS 2020, ICLR 2021, IJCAI 2021 and AAAI 2021.
• External reviewer for IJCAI 2020.
• Journal reviewer for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).

Academic Talks

• Bayesian Optimization Meets Bayesian Optimal Stopping, at Singapore-MIT Alliance, Future Urban Mobility Symposium 2019, Jan 28, 2019.
• Bayesian Optimization Meets Bayesian Optimal Stopping, at Learning and Vision Lab Group Seminar, NUS, ECE, Mar 8, 2019.
• R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games, at NUS Computing Research Week 2020, Aug 4, 2020 (top 3 student presenter).

Teaching

• Tutor for CS3244 Machine Learning, NUS School of Computing (Spring 2019)
• Teaching Assistant for CS1010E Programming Methodology, NUS School of Computing (3 semesters from 2012 to 2014)

Contact

daizhongxiang@comp.nus.edu.sg