Chrystal Chern

I am a dynamics researcher working at the intersection of mechanical systems, sensing, and scientific computing. My research centers on system identification: extracting models of physical systems from measured data, understanding when those models can and cannot be trusted, and using them to detect anomalies in physical behavior. In my doctoral work at UC Berkeley, I developed a digital twin framework for monitoring the health of civil infrastructure from vibration data, now deployed in a web-based monitoring platform used by the California Department of Transportation. As a postdoctoral researcher in the Dynamics Lab and STAIRLab at Berkeley, I have extended this work to new sensing and modeling problems, including identifying the causal structure of coupled human–object motion from multi-node IMU sensor networks, resolving ambiguity in the modal analysis of damped mechanical systems, and building open-source Python tools for system identification at scale. Before graduate school, I practiced as a building enclosure engineer at Simpson Gumpertz & Heger, where I performed standards-based acceptance testing, non-destructive evaluation, and failure diagnosis of installed building assemblies.

I am also committed to making research a transformative experience for students. As Program Strategist at Berkeley Discovery, I design and assess tiered mentorship programs that give students ownership over creation and innovation, and that work shapes how I supervise and mentor researchers in my own lab.