von Kármán Instructor at Caltech
I'm an incoming assistant professor at Georgia Tech in the Schools of Aerospace Engineering and Computational Science and Engineering. My research develops mathematical and computational methods that enable engineers to make better design decisions faster. My specialties are model reduction, data-driven modeling, scientific machine learning, and multi-fidelity methods. You can learn more on my research page.
Prior to joining the faculty at Georgia Tech, I held a von Kármán Instructorship at Caltech in the Department of Computing + Mathematical Sciences. I received my SB, SM, and PhD degrees from the MIT Department of Aeronautics & Astronautics. My research has been funded in parts by a Fulbright student grant, an NSF Graduate Research Fellowship and a Hertz Foundation Fellowship.
I am excited about mentoring and teaching the next generation of aerospace engineers and computational scientists, and I work to make my professional communities more equitable, diverse, and inclusive for generations to come. My service and mentoring efforts at Caltech were recognized with the departmental and division-wide DEI awards, and I also received an institute-wide teaching award from the Associated Students of Caltech (ASCIT).
Upcoming talks & activities
November 2022: I start as Assistant Professor at Georgia Tech on November 1.
Nov 29: I will give a virtual talk in the University of Waterloo Numerical Analysis and Scientific Computing seminar.
December 2022: I will give an invited tutorial on operator learning using neural networks in the Remote Colloquium on Vortex Dominated Flows on Dec 9.
September 2022: Our paper on the cost-accuracy trade-off in operator learning with neural networks has been accepted for publication in the Journal of Machine Learning. I presented this work at the SIAM conference on the Mathematics of Data Science. Talk recordings are available to registered attendees at the SIAM conference website.
August 2022: Received the Best Presentation Award in the postdoctoral category from the IACM Female Researchers Chapter at the World Congress on Computational Mechanics.
July 2022: New paper proposing new multi-fidelity estimators for global sensitivity analysis is out. When applied to the JW Space Telescope thermal models, our method reduces the computation time from more than 2 months to just 2 days.