Kian Hsiang Low
National University of Singapore, Carnegie Mellon University
Papers
27
Total Citations
951
H-Index
18
About
Kian Hsiang Low is a prominent robotics and artificial intelligence researcher whose work sits at the intersection of multi-robot systems, environmental sensing, and probabilistic machine learning. His research has made foundational contributions to autonomous exploration, adaptive path planning, and Gaussian process-based modeling, with his most cited papers collectively amassing over 660 citations. Low's early work established novel architectures for mobile robot coordination, including hybrid deliberative-reactive planning systems and distributed task allocation frameworks for autonomic sensor networks. Building on these foundations, he pioneered adaptive multi-robot exploration strategies that intelligently balance wide-area coverage with targeted hotspot sampling — a practical breakthrough for large-scale environmental monitoring. His information-theoretic approaches to path planning enabled robots to make principled decisions under uncertainty, significantly reducing computational complexity. A hallmark of Low's later contributions is his sophisticated application of Gaussian processes to real-world challenges, including decentralized environmental field classification, persistent robot localization, and spatiotemporal traffic prediction for Mobility-on-Demand systems. His 2015 work integrating Gaussian process decentralized data fusion into urban mobility platforms exemplifies his ability to translate theoretical advances into impactful applications. Across two decades, Low has consistently advanced the frontier of intelligent, coordinated robotic sensing systems.
Research Focus
Key Achievements
Top Papers
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- 2Adaptive multi-robot wide-area exploration and mapping94 citations · 2008
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- 6A hybrid mobile robot architecture with integrated planning and control63 citations · 2002
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- 9Adaptive Sampling for Multi-Robot Wide-Area Exploration46 citations · 2007
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