Weizhe Chen
Papers
9
Total Citations
106
H-Index
5
About
Weizhe Chen is a leading researcher in robotic information gathering (RIG), multi-objective planning, and adaptive environmental monitoring. His work addresses the fundamental challenge of enabling autonomous robots to efficiently collect informative data under real-world constraints, such as limited time, non-stationary environments, and streaming data. Chen’s most impactful contribution is the Pareto Monte Carlo Tree Search (P-MCTS), introduced in his 2019 paper (50 citations), which provides an anytime, multi-objective planning framework for robots that must simultaneously explore and exploit features of interest. He further advanced the field with the Attentive Kernel (AK) for Gaussian processes (16 citations), enabling more accurate spatial modeling in heterogeneous environments, and adaptive RIG methods using non-stationary GPs (17 citations). His work on long-term autonomous ocean monitoring and robust planning in the presence of outliers demonstrates a commitment to real-world deployment. With over 100 total citations and a growing portfolio of novel planners—including DiSProD for continuous state-action spaces—Chen is shaping the future of autonomous scientific exploration, from underwater sampling to environmental surveillance.
Research Focus
Key Achievements
Top Papers
- 1Pareto Monte Carlo Tree Search for Multi-Objective Informative Planning50 citations · 2019
- 2Adaptive Robotic Information Gathering via non-stationary Gaussian processes17 citations · 2023
- 3AK: Attentive Kernel for Information Gathering16 citations · 2022
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- 6Long-Term Autonomous Ocean Monitoring with Streaming Samples5 citations · 2019
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- 8Informative Planning in the Presence of Outliers2 citations · 2022
- 9Pareto Monte Carlo Tree Search for Multi-Objective Informative Planning2 citations · 2021