Jun Xu
Papers
3
Total Citations
68
H-Index
3
About
Jun Xu is a leading researcher at the intersection of cyber-physical systems, robotics, and machine learning safety. His most impactful work focuses on anomaly detection for mobile robots, where he pioneered methods to identify sensor and actuator misbehaviors that threaten the safety of unmanned vehicles. In his seminal 2018 paper, "RoboADS: Anomaly Detection Against Sensor and Actuator Misbehaviors in Mobile Robots," which has garnered 49 citations, Xu introduced a framework that exploits the tight coupling between cyberspace and physical dynamics to detect attacks that transcend traditional cyber defenses. His earlier 2017 work, with 16 citations, further advanced this area by demonstrating how physical dynamics can be leveraged to identify active attacks on mobile robots, addressing critical safety gaps in autonomous systems. More recently, Xu has pushed the boundaries of uncertainty quantification with his 2025 paper on "Online scalable Gaussian processes with conformal prediction for guaranteed coverage," which tackles the challenge of reliable uncertainty estimates in safety-critical applications like robotics and healthcare. His work is essential reading for researchers in robotics, control systems, and trustworthy AI.
Research Focus
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