Elizabeth Qian

von Kármán Instructor at Caltech

I am a von Kármán Instructor in the Department of Computing + Mathematical Sciences at Caltech. My research lies at the intersection of computational science and engineering application, and 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 engineering 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. As a graduate student, I was supported by the NSF Graduate Research Fellowship and the Fannie and John Hertz Foundation Fellowship. Prior to starting graduate studies, I spent a year on a Fulbright at RWTH Aachen University working with Karen Veroy-Grepl and Martin Grepl. I obtained my SB and SM degrees in Aerospace Engineering from MIT in 2014 and 2017.

Upcoming talks & activities

June 2022: I will attend the workshop celebrating 30 years of Acta Numerica at the Banach Center in Będlewo, Poland from June 26 - July 2.

July 2022: (1) I will virtually present our paper on reduced operator inference for nonlinear PDEs at the hybrid SIAM Annual Meeting.

(2) I will contribute a video presentation on Reduced operator inference for nonlinear PDEs to the virtual World Congress on Computational Mathematics.

September 2022: I will give an invited plenary talk at the Symposium on Inverse Problems in Potsdam, Germany (Sept 19-21, 2022). My talk will discuss our work establishing connections between balanced truncation and Bayesian inference.

Recent news

June 2022: I presented our work on the cost-accuracy trade-off in learning neural operators at the US National Congress on Theoretical and Applied Mechanics in Austin, TX.

Received a 2021-2022 ASCIT Teaching Award from the Associated Students of Caltech. Nomination and selection for this award is run entirely by students.

I presented our work on Balanced Truncation for Bayesian Inference in the Oxford Computational Mathematics & Applications Seminar on June 2.

May 2022: My contributions toward advancing DEI at Caltech have been recognized with the departmental Gradient for Change Award as well as the division-wide New Horizons Award.

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