LOW Kian Hsiang

Associate Professor
Deputy Director of AI Research & Technology, AI Singapore
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)
COM2-02-58
651 64719

http://www.comp.nus.edu.sg/~lowkh/research.html

Research Areas

  • Artificial Intelligence

Research Interests

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

Profile

Dr. Bryan Low is an Associate Professor at the Department of Computer Science of the National University of Singapore and the Deputy Director of AI Research and Technology 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 also served an organizing chair for the IEEE RAS Summer School on Multi-Robot Systems in Jun 2016 and the AI Summer School in Jul 2019. Dr. Low has also served as associate editors, 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, NIPS, 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.

Current Projects

  • Bayesian deep learning, data-efficient machine learning (Bayesian optimization, active learning), privacy-preserving machine learning, probabilistic machine learning, Bayesian non-parametric models, Gaussian processes, planning under uncertainty, reinforcement learning, multi-agent/robot systems, data fusion, computational sustainability.

Selected Publications

  • Dmitrii Kharkovskii, Chun Kai Ling, and Kian Hsiang Low (2020). Nonmyopic Gaussian Process Optimization with Macro-Actions. In Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS-20).

  • Tong Teng, Jie Chen, Yehong Zhang, and Kian Hsiang Low (2020). Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI-20) [20.6% Acceptance Rate].

  • Haibin Yu, Yizhou Chen, Zhongxiang Dai, Kian Hsiang Low, and Patrick Jaillet (2019). Implicit Posterior Variational Inference for Deep Gaussian Processes. In Advances in Neural Information Processing Systems 32: 33rd Annual Conference on Neural Information Processing Systems (NeurIPS’19), pages 14475-14486 [3% Acceptance Rate (Spotlight Presentation)].

  • Yehong Zhang, Zhongxiang Dai, and Kian Hsiang Low (2019). Bayesian Optimization with Binary Auxiliary Information. In Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI-19) [26.2% Acceptance Rate (Plenary Talk)].

  • Zhongxiang Dai, Haibin Yu, Kian Hsiang Low, and Patrick Jaillet (2019). Bayesian Optimization Meets Bayesian Optimal Stopping. In Proceedings of the 36th International Conference on Machine Learning (ICML-19), pages 1496-1506 [22.6% Acceptance Rate].

  • Quang Minh Hoang, Trong Nghia Hoang, Kian Hsiang Low, and Carleton Kingsford (2019). Collective Model Fusion for Multiple Black-Box Experts. In Proceedings of the 36th International Conference on Machine Learning (ICML-19), pages 2742-2750 [22.6% Acceptance Rate].

  • Trong Nghia Hoang, Quang Minh Hoang, Kian Hsiang Low, and Jonathan P. How (2019). Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI-19), pages 7850-7857 [16.2% Acceptance Rate].

  • Trong Nghia Hoang, Quang Minh Hoang, Ruofei Ouyang, and Kian Hsiang Low (2018). Decentralized High-Dimensional Bayesian Optimization with Factor Graphs. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI-18), pages 3231-3238 [24.55% Acceptance Rate].

  • Ruofei Ouyang and Kian Hsiang Low (2018). Gaussian Process Decentralized Data Fusion Meets Transfer Learning in Large-Scale Distributed Cooperative Perception. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI-18), pages 3876-3883 [24.55% Acceptance Rate].

  • Erik Daxberger and Kian Hsiang Low (2017). Distributed Batch Gaussian Process Optimization. In Proceedings of the 34th International Conference on Machine Learning (ICML-17), pages 951-960 [25.5% Acceptance Rate].

  • Quang Minh Hoang, Trong Nghia Hoang, and Kian Hsiang Low (2017). A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression. In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI-17), pages 2007-2014 [24.6% Acceptance Rate].

  • Trong Nghia Hoang, Quang Minh Hoang, and Kian Hsiang Low (2016). A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models. In Proceedings of the 33rd International Conference on Machine Learning (ICML-16), pages 382-391 [24.3% Acceptance Rate].

  • Chun Kai Ling, Kian Hsiang Low, and Patrick Jaillet (2016). Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond. In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16), pages 1860-1866 [25.75% Acceptance Rate].

  • Yehong Zhang, Trong Nghia Hoang, Kian Hsiang Low, and Mohan Kankanhalli (2016). Near-Optimal Active Learning of Multi-Output Gaussian Processes. In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16), pages 2351-2357 [25.75% Acceptance Rate].

  • Jie Chen, Kian Hsiang Low, Patrick Jaillet, and Yujian Yao (2015). Gaussian Process Decentralized Data Fusion and Active Sensing for Spatiotemporal Traffic Modeling and Prediction in Mobility-on-Demand Systems. In IEEE Transactions on Automation Science and Engineering (Special Issue on Networked Cooperative Autonomous Systems), volume 12, issue 3, pages 901-921.

  • Trong Nghia Hoang, Quang Minh Hoang, and Kian Hsiang Low (2015). A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data. In Proceedings of the 32nd International Conference on Machine Learning (ICML-15), pages 569-578 [26.0% Acceptance Rate].

  • Quoc Phong Nguyen, Kian Hsiang Low, and Patrick Jaillet (2015). Inverse Reinforcement Learning with Locally Consistent Reward Functions. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, R. Garnett, editors, Advances in Neural Information Processing Systems 28: 29th Annual Conference on Neural Information Processing Systems (NeurIPS’15), pages 1747-1755 [21.9% Acceptance Rate].

  • Kian Hsiang Low, Jiangbo Yu, Jie Chen, and Patrick Jaillet (2015). Parallel Gaussian Process Regression for Big Data: Low-Rank Representation Meets Markov Approximation. In Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI-15), pages 2821-2827 [26.67% Acceptance Rate].

  • Trong Nghia Hoang, Kian Hsiang Low, Patrick Jaillet, and Mohan Kankanhalli (2014). Nonmyopic ε-Bayes-Optimal Active Learning of Gaussian Processes. In Proceedings of the 31st International Conference on Machine Learning (ICML-14), pages 739-747 [25.0% Acceptance Rate].

  • Nuo Xu, Kian Hsiang Low, Jie Chen, Keng Kiat Lim, and Etkin B. Ozgul (2014). GP-Localize: Persistent Mobile Robot Localization using Online Sparse Gaussian Process Observation Model. In Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI-14), pages 2585-2592 [28.3% Acceptance Rate].

  • Ruofei Ouyang, Kian Hsiang Low, Jie Chen, and Patrick Jaillet (2014). Multi-Robot Active Sensing of Non-Stationary Gaussian Process-Based Environmental Phenomena. In Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-14), pages 573-580 [23.8% Acceptance Rate].

  • Jie Chen, Kian Hsiang Low, and Colin Keng-Yan Tan (2013). Gaussian Process-Based Decentralized Data Fusion and Active Sensing for Mobility-on-Demand System. In Proceedings of the Robotics: Science and Systems Conference (RSS-13) [30.1% Acceptance Rate].

  • Trong Nghia Hoang and Kian Hsiang Low (2013). Interactive POMDP Lite: Towards Practical Planning to Predict and Exploit Intentions for Interacting with Self-Interested Agents. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI-13), pages 2298-2305 [13.2% Acceptance Rate (Oral Presentation)].

  • Trong Nghia Hoang and Kian Hsiang Low (2013). A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI-13), pages 1394-1400 [28.0% Acceptance Rate].

  • Nannan Cao, Kian Hsiang Low, and John M. Dolan (2013). Multi-Robot Informative Path Planning for Active Sensing of Environmental Phenomena: A Tale of Two Algorithms. In Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-13), pages 7-14 [22.9% Acceptance Rate].

  • Jie Chen, Nannan Cao, Kian Hsiang Low, Ruofei Ouyang, Colin Keng-Yan Tan, and Patrick Jaillet (2013). Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations. In Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI-13), pages 152-161 [31.3% Acceptance Rate].

  • Jie Chen, Kian Hsiang Low, Colin Keng-Yan Tan, Ali Oran, Patrick Jaillet, John M. Dolan, and Gaurav S. Sukhatme (2012). Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena. In Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence (UAI-12), pages 163-173 [31.6% Acceptance Rate].

  • Prabhu Natarajan, Trong Nghia Hoang, Kian Hsiang Low, and Mohan Kankanhalli (2012). Decision-Theoretic Approach to Maximizing Observation of Multiple Targets in Multi-Camera Surveillance. In Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-12), pages 155-162 [20.4% Acceptance Rate].

  • Kian Hsiang Low, Jie Chen, John M. Dolan, Steve Chien, and David R. Thompson (2012). Decentralized Active Robotic Exploration and Mapping for Probabilistic Field Classification in Environmental Sensing. In Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-12), pages 105-112 [20.4% Acceptance Rate].

  • Kian Hsiang Low, John M. Dolan, and Pradeep K. Khosla (2011). Active Markov Information-Theoretic Path Planning for Robotic Environmental Sensing. In Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-11), pages 753-760 [22.1% Acceptance Rate].

  • Kian Hsiang Low, John M. Dolan, and Pradeep K. Khosla (2009). Information-Theoretic Approach to Efficient Adaptive Path Planning for Mobile Robotic Environmental Sensing. In Proceedings of the 19th International Conference on Automated Planning and Scheduling (ICAPS-09), pages 233-240 [33.9% Acceptance Rate].

  • Kian Hsiang Low, John M. Dolan, and Pradeep K. Khosla (2008). Adaptive Multi-Robot Wide-Area Exploration And Mapping. In Proceedings of the 7th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-08), pages 23-30 [22.2% Acceptance Rate].

  • Kian Hsiang Low, Wee Kheng Leow, and Marcelo H. Ang, Jr. (2006). Autonomic Mobile Sensor Network with Self-Coordinated Task Allocation and Execution. In IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews (Special Issue on Engineering Autonomic Systems), volume 36, issue 3, pp. 315-327 [ 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 ].

  • Kian Hsiang Low, Wee Kheng Leow, and Marcelo H. Ang, Jr. (2005). An Ensemble of Cooperative Extended Kohonen Maps for Complex Robot Motion Tasks. In Neural Computation, volume 17, issue 6, pages 1411-1445.

  • Kian Hsiang Low, Wee Kheng Leow , and Marcelo H. Ang, Jr. (2004). Task Allocation via Self-Organizing Swarm Coalitions in Distributed Mobile Sensor Network. In Proceedings of the 19th National Conference on Artificial Intelligence (AAAI-04), pages 28-33 [26.7% Acceptance Rate].

  • Kian Hsiang Low, Wee Kheng Leow , and Marcelo H. Ang, Jr. (2003). Action Selection for Single- and Multi-Robot Tasks Using Cooperative Extended Kohonen Maps. In Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI-03), pages 1505-1506 [27.6% Acceptance Rate].

  • Kian Hsiang Low, Wee Kheng Leow , and Marcelo H. Ang, Jr. (2002). A Hybrid Mobile Robot Architecture with Integrated Planning and Control. In Proceedings of the 1st International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-02), pages 219-226 [26% Acceptance Rate].

Awards & Honours

  • IEEE Robotics & Automation Society (RAS) Distinguished Lecturer for the IEEE RAS Technical Committee on Multi-Robot Systems, Mar 2019
  • Invited to serve as a World Economic Forum’s Global Future Councils Fellow for the Council on the Future of Artificial Intelligence and Robotics, Sep 2016 – Jun 2018
  • Faculty Teaching Excellence Award in School of Computing, National University of Singapore, Aug 2017 - Jul 2018
  • Best PhD Forum Paper Award in 6th ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC’12) won by my PhD student, Prabhu Natarajan, Nov 2012
  • Featured in Association for Unmanned Vehicle Systems International (AUVSI) Unmanned Systems Magazine "Ones to Watch" June 2010 issue
  • Andrew P. Sage Best Transactions Paper Award for the best paper (first author) published in all 3 of the IEEE Transactions on Systems, Man, and Cybernetics - Parts A, B, and C in 2006
  • NUS Overseas Graduate Scholarship for Ph.D. studies in Carnegie Mellon University, 2004-2009
  • Winner of Singapore Computer Society Prize for Best M.Sc. Thesis (among 81 graduates of M.Sc. by research) in SOC, NUS, 2002-2003

Teaching (2020/2021)

  • CS3244: Machine Learning