von Kármán Postdoctoral Instructor at Caltech
I am a von Kármán Postdoctoral Instructor in the Department of Computing + Mathematical Sciences at Caltech. My research 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 physical 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. My thesis developed a new scientific machine learning method for learning efficient surrogate models for systems governed by nonlinear PDEs, and demonstrated the new method on a large-scale combustion simulation.
As a graduate student, I was the recipient of the NSF Graduate Research Fellowship and the Fannie and John Hertz Foundation Fellowship. Before starting graduate school, I spent a year on a Fulbright at RWTH Aachen University working with Karen Veroy-Grepl and Martin Grepl on using reduced basis methods in PDE-constrained optimization. I obtained my SB and SM degrees in Aerospace Engineering from MIT in 2014 and 2017.
Upcoming talks & activities
September 2021: Presenting 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.
October 2021: Invited to present our work on Balanced truncation for Bayesian inference at the UC San Diego Center for Control Systems and Dynamics Seminar on Oct 1.
Attending the NextProf Nexus workshop at the University of Michigan Oct 4-7.
December 2021: Attending the Women in Inverse Problems virtual workshop hosted by Banff International Research Station Dec 5-10.
July 2021: I gave a talk at SIAM AN on July 22 on Model reduction of linear dynamical systems via balancing for Bayesian inference. This work is a collaboration that grew out of the ICERM Semester Program on Model and Dimension Reduction.
April 2021: Invited to give a SCAN Seminar at Cornell on April 19.