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.
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.
January 2021: Excited to be joining the Diversity, Equity, and Inclusion (DEI) Steering Committee in my new department.
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.