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.
I welcome emails from Caltech undergraduate students interested in working with me in Summer 2021. The best time to get in touch would be at the start of Winter Term 2021 to discuss potential opportunities.
January 2021: Happy New Year! January 1st is my start date for my new gig as von Karman Postdoctoral Instructor at Caltech. After nearly a decade at MIT, I'm looking forward to new opportunities and new challenges.
December 2020: Very happy to have successfully defended my PhD thesis on December 7.