von Kármán Instructor at Caltech
I am a von Kármán Instructor in the Department of Computing + Mathematical Sciences at Caltech. My research lies at the intersection of computational science and engineering application, and is motivated by the need for computational methods used in engineering decision-making to be efficient and scalable. In particular, I am interested in model reduction and scientific machine learning for engineering systems, and in multi-fidelity formulations for uncertainty quantification and optimization.
I completed my PhD in Computational Science & Engineering at MIT, where I worked with Karen Willcox as a student in both the Center for Computational Science and Engineering and the Department of Aeronautics & Astronautics. As a graduate student, I was supported by the NSF Graduate Research Fellowship and the Fannie and John Hertz Foundation Fellowship. Prior to starting graduate studies, I spent a year on a Fulbright at RWTH Aachen University working with Karen Veroy-Grepl and Martin Grepl. I obtained my SB and SM degrees in Aerospace Engineering from MIT in 2014 and 2017.
Upcoming talks & activities
December 2021: Attending the Women in Inverse Problems virtual workshop hosted by Banff International Research Station Dec 5-10.
October 2021: Attended the NextProf Nexus workshop at the University of Michigan Oct 4-7.
I presented our work on Balanced truncation for Bayesian inference at the UC San Diego Center for Control Systems and Dynamics Seminar on Oct 1. This work is a collaboration that grew out of the ICERM Semester Program on Model and Dimension Reduction.
September 2021: I presented our new function-space formulation of Operator Inference and Lift & Learn for learning reduced models for nonlinear PDEs at the hybrid MMLDT-CSET 2021 conference Sept 26-29.