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About

Qihao Shan is a leading researcher in swarm robotics, specializing in collective decision-making and estimation. His work focuses on enabling large groups of simple robots to solve complex problems through distributed intelligence, particularly in noisy and uncertain environments. Shan's major contributions include developing novel Bayesian hypothesis testing frameworks for collective decision-making, which allow swarms to converge on optimal solutions using only local interactions. His most-cited paper, "Collective Decision Making in Swarm Robotics with Distributed Bayesian Hypothesis Testing" (2020), has garnered 19 citations and established a foundation for multi-option collective perception. He further advanced the field with "Discrete collective estimation in swarm robotics with distributed Bayesian belief sharing" (2021, 18 citations), extending binary decision-making to estimate fill ratios. His work on ranked voting systems (2021, 12 citations) and noise-resistant preference learning (2022) demonstrates his commitment to creating scalable, robust swarm algorithms. Shan's research is pivotal for real-world applications where reliability and scalability are critical, making him a notable figure in the advancement of swarm intelligence.