Kevin Carlberg

Kevin Carlberg is an Affiliate Associate Professor of Applied Mathematics and Mechanical Engineering at the University of Washington. He currently holds leadership roles in AI Research Science at Meta Reality Labs and has prior experience at Sandia National Laboratories. His research focuses on contextual AI, computational physics, and model reduction techniques. He holds a PhD in Aeronautics and Astronautics from Stanford University (2011), an MS from Stanford (2006), and a BS from Washington University in St. Louis (2005).

Carlberg leads interdisciplinary teams to develop AI-driven methodologies for extreme-scale simulations and wearable technologies. His work emphasizes reducing computational complexity for high-consequence applications, such as national security and VR/MR devices. Key areas include nonlinear model reduction, uncertainty quantification, and data-driven time-parallelism. He has pioneered techniques like the network uncertainty quantification (NetUQ) method and deep neural network-based error modeling.

His research has been published in top journals like Computer Methods in Applied Mechanics and Engineering and Journal of Computational Physics, focusing on ROMs, machine learning integration, and high-performance computing. His contributions address challenges in Kolmogorov widths and real-time simulation requirements for large-scale systems.