Yan Pei

The University of Texas at Austin, University of Aizu

Papers

Total Citations

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About

Yan Pei’s research lies at the intersection of state estimation, autonomous systems, and energy-efficient computing. His work on Kalman filtering—a foundational technique for signal processing and vehicle control—has been widely recognized, with his 2017 introduction to the topic accumulating 19 citations and helping bridge classical estimation theory with modern computer system applications like processor voltage and frequency management. Pei has also made significant contributions to visual SLAM (Simultaneous Localization and Mapping), a critical technology for robotics and autonomous driving. His 2020 paper on principled approximation in visual SLAM (6 citations) introduces a methodology to reduce time and energy consumption by leveraging approximate computing, addressing the real-world constraints of autonomous agents navigating uncertain environments. Looking ahead, Pei’s 2025 work on enhancing environmental modeling and maximum diffusion reinforcement learning through evolutionary computation (2 citations) signals his ongoing commitment to optimizing learning algorithms for complex, dynamic systems. Through these contributions, Pei demonstrates a clear focus on making autonomous systems both more capable and more resource-efficient, a balance that is increasingly vital in the age of edge computing and mobile robotics.