Bryan Low

Education

• Ph.D. (Electrical & Computer Engineering, Carnegie Mellon University, 2009)
• B.Sc. (Computer Science, National University of Singapore, 2001)
• M.Sc. (Computer Science, National University of Singapore, 2002)

Dr. Bryan Low is an Associate Professor of Computer Science at the National University of Singapore and the Director of AI Research at AI Singapore. He obtained the B.Sc. (Hons.) and M.Sc. degrees in Computer Science from National University of Singapore, Singapore, in 2001 and 2002, respectively, and the Ph.D. degree in Electrical and Computer Engineering from Carnegie Mellon University, Pittsburgh, Pennsylvania, in 2009. His research interests include probabilistic & automated machine learning, planning under uncertainty, and multi-agent/robot systems.

Dr. Low is the recipient of the (1) Andrew P. Sage Best Transactions Paper Award for the best paper published in all 3 of the IEEE Transactions on Systems, Man, and Cybernetics - Parts A, B, and C in 2006; (2) National University of Singapore Overseas Graduate Scholarship for Ph.D. studies in Carnegie Mellon University (CMU) in 2004-2009; (3) Singapore Computer Society Prize for Best M.Sc. Thesis in School of Computing, National University of Singapore in 2003; and (4) Faculty Teaching Excellence Award in School of Computing, National University of Singapore in 2017-2018.

Dr. Low has served as a World Economic Forum’s Global Future Councils Fellow for the Council on the Future of Artificial Intelligence and Robotics from Sep 2016 to Jun 2018 and an IEEE Robotics & Automation Society (RAS) Distinguished Lecturer for the IEEE RAS Technical Committee on Multi-Robot Systems in Mar 2019. He has served as an organizing chair for the IEEE RAS Summer School on Multi-Robot Systems in Jun 2016, the AI Summer Schools in Jul 2019 and Aug 2020, and the NeurIPS 2021 Workshop on New Frontiers in Federated Learning. Dr. Low has also served as associate editors, area chairs and program committee members, and reviewers for premier AI (specifically, multiagent systems, AI planning, robotics, machine learning) conferences: IJCAI, AAAI, ECAI, AAMAS, ICAPS, RSS, IROS, ICRA, CoRL, NeurIPS, ICML, AISTATS, ICLR and journals: TKDE, JMLR, JAIR, MLJ, TNNLS, T-ASE, IJRR, T-RO, AURO, JFR, TOSN, JAAMAS. He was the top 5% reviewer for ICML 2019, top 33% reviewer for ICML 2020, and an expert reviewer for ICML 2021.

Research Areas

• Artificial Intelligence
• Decision Making & Planning
• Machine Learning
• Multi-Agent Systems & Algorithmic Game Theory
• Robotics
• Trustworthy AI

Research Interests

• Probabilistic Machine Learning (e.g., Bayesian deep learning, Gaussian process, Bayesian non-parametric models)
• Data-Efficient Machine Learning (e.g., Bayesian optimization, meta-learning, active learning, and adaptive sampling)
• Multi-Party Machine Learning (e.g., federated/distributed/collaborative learning, decentralized data fusion, privacy-preserving machine learning)
• Reinforcement Learning and Multi-Agent Reinforcement Learning
• Planning Under Uncertainty
• Multi-Agent/Robot Systems
• Computational Sustainability

Selected Publications

• Rachael Hwee Ling Sim, Yehong Zhang, Bryan Kian Hsiang Low, and Patrick Jaillet (2021). Collaborative Bayesian Optimization with Fair Regret. In Proceedings of the 38th International Conference on Machine Learning (ICML-21), pages 9691-9701.
• Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, and Patrick Jaillet (2021). Value-at-Risk Optimization with Gaussian Processes. In Proceedings of the 38th International Conference on Machine Learning (ICML-21), pages 8063-8072.
• Chi Thanh Lam, Trong Nghia Hoang, Bryan Kian Hsiang Low, and Patrick Jaillet (2021). Model Fusion for Personalized Learning. In Proceedings of the 38th International Conference on Machine Learning (ICML-21), pages 5948-5958.
• Quoc Phong Nguyen, Bryan Kian Hsiang Low, and Patrick Jaillet (2021). Learning to Learn with Gaussian Processes. In Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI-21).
• Quoc Phong Nguyen, Zhaoxuan Wu, Bryan Kian Hsiang Low, and Patrick Jaillet (2021). Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization. In Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI-21).