Elizabeth Qian

Assistant Professor at Georgia Tech

I'm an 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

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.

February 2023: I will attend SIAM CSE 23 in Amsterdam (Feb 26 - Mar 3) and will present our work on multifidelity global sensitivity analysis for the JW Space Telescope.

April 2023: I will give an invited plenary at the MASCOT-NUM 2023 conference in Le Croisic, France (April 3-6).

Recent news

November 2022: PhD student Tomoki Koike is the inaugural member of the Aerospace Computational Engineering (ACE) Group at Georgia Tech. Tomoki completed his BS in Aerospace Engineering at Purdue and his research interests are in model reduction and control. Welcome Tomoki!

September 2022: Our paper on the cost-accuracy trade-off in operator learning with neural networks has appeared in the Journal of Machine Learning.

August 2022: Received the Best Presentation Award in the postdoctoral category from the IACM Female Researchers Chapter at the World Congress on Computational Mechanics.

News Archive