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About

Dingkang Wang is a researcher specializing in microelectromechanical systems (MEMS)-based sensing, miniaturized LiDAR technologies, and robotic perception. His work sits at the intersection of optical engineering, sensor design, and autonomous systems, with a particular focus on developing compact, high-performance laser scanning solutions for robotics and autonomous vehicles. Wang's most significant contributions center on advancing MEMS mirror technology for LiDAR applications. His 2021 paper introducing a miniature LiDAR with a detached MEMS scanner for micro-robotics — his most cited work with 45 citations — demonstrated how bird- and insect-scale robots could be equipped with functional ranging systems. His complementary work on monolithic forward-view scanners and electrothermal tripod MEMS mirrors further pushed the boundaries of what compact, low-cost LiDAR systems can achieve in terms of scanning angle and omnidirectional coverage. Beyond hardware, Wang has made meaningful contributions to intelligent sensing strategies, including adaptive foveal depth sensing that mirrors human visual attention and directionally controlled time-of-flight ranging. His 2024 work on adaptive LiDAR decoupled from robot-camera geometry signals continued evolution toward smarter, more autonomous perception systems. With nearly 130 cumulative citations, Wang's research is shaping the future of lightweight, intelligent sensing for next-generation robotics.