Shan Lin

Papers

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About

Shan Lin is a researcher whose work spans wireless sensor networks, mobile energy systems, and, more recently, surgical computer vision. Lin is perhaps best known for pioneering contributions to mobile charger scheduling and charging path optimization — problems that arise when autonomous agents such as drones, robots, and vehicles must receive wireless energy delivery while completing assigned tasks. The 2016 paper "Charge Me If You Can," the most cited work in Lin's portfolio with 48 citations, established a foundational optimization framework for charger dispatch and scheduling in mobile networks, a contribution later extended in a 2022 follow-up that refined and generalized the charging path formulations. Earlier work from 2015 addressed dynamic charger scheduling within heterogeneous wireless sensor networks, framing sensor recharging as a real-time scheduling problem — an insight that helped shape subsequent research in sustainable network design. Most recently, Lin has branched into medical imaging and robotics, contributing to "BASED," a neural radiance field-based approach for reconstructing deformable surgical scenes from endoscopic video, with direct implications for autonomous robotic surgery. This trajectory reflects a researcher consistently drawn to optimization challenges at the intersection of robotics, networking, and autonomy.