Kevin Carlberg

Kevin Carlberg


My research combines computational mechanics, machine learning, numerical linear algebra, and high-performance computing. The objective is to discover structure in data to drastically reduce the cost of simulating nonlinear dynamical systems at extreme scale. This enables high-fidelity models to be used for many-query applications such as design optimization and uncertainty quantification.

In 2011, I received my PhD in Aeronautics and Astronautics (with a PhD minor in ICME) from Stanford University, where Charbel Farhat was my research adviser. From 2011 to 2014, I was the President Harry S. Truman Fellow at Sandia National Laboratories in Livermore, CA. Since 2014, I have been a Principal Member of Technical Staff at Sandia.

My research interests include nonlinear model reduction, machine learning, computational mechanics, high-performance computing, numerical linear algebra, uncertainty quantification, time-parallel methods, Krylov-subspace methods, structure-preserving approximations, and numerical optimization.

Recent publication highlights