Faculty Profiles

To enable—or accelerate—computationally expensive engineering tasks, Kramer develops and analyzes new methods and algorithms based on models that reduce computational complexity. For example, his computational methods innovate in aspects of learning, control, design and uncertainty quantification of complex and large-scale systems. His research group has applied these methods to digital twins, rocket combustion, capillary wave turbulence, space weather, soft robotics, metal additive manufacturing, multidisciplinary design optimization, semiconductor manufacturing, radiation hardening of electronics, and metamaterial design.

Capsule Bio:

Professor Kramer joined UC San Diego in 2019 as an Assistant Professor and since 2025 is a tenured Associate Professor. Prior to joining UC San Diego, he spent four years as a Postdoctoral Associate in the Department of Aeronautics and Astronautics and the Aerospace Computational Design Lab at the Massachusetts Institute of Technology. He received his M.Sc. (2011) and Ph.D. (2015) in Mathematics from Virginia Tech. Prior to that, he studied Mathematics in Technology and Mechanical Engineering at the University of Karlsruhe (now KIT), Germany. He is a member of the Society for Industrial and Applied Mathematics (SIAM), and a Senior Member of AIAA where he also serves on the Nondeterministic Approaches Technical Committees, and a Senior Member of IEEE. He is a 2022 NSF CAREER Awardee and won a DoD Newton Award in 2020. From 2023-present he serves as an Associate Editor for the SIAM/ASA Journal of Uncertainty Quantification. His research is funded by the Office of Naval Research, the Air Force Office of Scientific Research, the Defense Advanced Research Projects Agency, the Department of Energy, the National Science Foundation as well as industrially through Samsung Electronics Co. and ASML.