Research Areas Computer vision imaging systems medical imaging image analysis machine learning human-AI interaction augmented reality computer-assisted interventions

Mathias Unberath is the John C. Malone Associate Professor in the Department of Computer Science and holds a secondary appointment at the Johns Hopkins School of Medicine in ophthalmology and otolaryngology, head and neck surgery. He is also a core faculty member of the Laboratory for Computational Sensing and Robotics, the Malone Center for Engineering in Healthcare, and an affiliate faculty member in the Institute of Assured Autonomy.

With his group—the Advanced Robotics and Computationally AugmenteD Environments (ARCADE) Lab—Mathias builds the future of computer-assisted medicine by creating collaborative intelligent systems. Through synergistic research on imaging, computer vision, machine learning, and interaction design, he invents human-centered solutions that are embodied in emerging technology such as mixed reality and robotics.

Previously, Mathias was an assistant research professor in computer science and a postdoctoral fellow in the Laboratory for Computational Sensing and Robotics at Hopkins. He completed his PhD in computer science at the Friedrich-Alexander-Universität Erlangen-Nürnberg from which he graduated summa cum laude in 2017. While completing a bachelor’s in physics and master’s in optical technologies at FAU Erlangen, Mathias studied at the University of Eastern Finland as an ERASMUS scholar in 2011 and joined Stanford University as a DAAD Fellow throughout 2014.

Mathias has published more than 100 journal and conference articles, and has received numerous awards, grants, and fellowships, such as being named an NIH NIBIB R21 Trailblazer and a recipient of the NSF CAREER Award, Google Research Scholar Award, JHU Career Impact Award, and inaugural JHU DSAI Junior Faculty Award.

He teaches CS482/682 Machine Learning: Deep Learning, CS486/686 Artificial Intelligent System Design and Development, and CS486/686 Interpretable Machine Learning Design.