von Karman Postdoctoral Instructor at Caltech
I am a von Karman 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.
In 2020, I completed my PhD in Computational Science & Engineering at MIT, where I was supervised by Karen Willcox and affiliated with the Center for Computational Science and Engineering as well as 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.
For prospective students:
Please note that I will not be supervising graduate students in my postdoctoral appointment at Caltech and I am not involved in Caltech CMS graduate admissions in any way.
Caltech undergraduate students: the SURF deadline for 2021 has passed. If you are interested in working with me in summer 2022, the best time to reach out would be in late 2021.
I am not able to take on high school interns or undergraduate student researchers from outside Caltech.
April 2021: Invited to give a SCAN Seminar at Cornell on April 19.
March 2021: At SIAM CSE 21, I co-organized a mini-symposium on "Dimension reduction for Bayesian inverse problems" and also presented my thesis work. Recordings of all talks will be available to conference registrants until June 4 via the conference platform.
February 2021: New pre-print on "Reduced operator inference for nonlinear partial differential equations" with Ionut-Gabriel Farcas and Karen Willcox is up on arXiv. This work presents a new formulation for learning from data the operators of a reduced model that maps between Hilbert spaces, yielding speed-ups of 5-6 orders of magnitude for a 3D combustion simulation.