Elizabeth Qian
Assistant Professor at Georgia Tech
Assistant Professor at Georgia Tech
I am serving as a guest editor for a special issue of Structural and Multidisciplinary Optimization (SMO) on "Reduced Order Modeling, Generative AI, and SciML in Digital Twins". Submissions are currently being accepted until October 31 (deadline extended from July 31).
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. I also currently hold a visiting appointment as a Hans Fischer Fellow at the Technical University of Munich.
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 teaching contributions have previously been recognized with departmental and division-wide DEI awards, as well as an institute-wide teaching award.
March 2026: I am co-organizing a workshop on multi-fidelity methods for fusion plasma physics at the Institute for Pure and Applied Mathematics (IPAM) in Los Angeles from March 23-26, 2026.
June 2026: I am co-organizing the 3rd AIAA Workshop on Multifidelity Methods for Design and Uncertainty Quantification, in conjunction with AIAA Aviation Forum 2026. More details regarding the program and registration coming soon...
July 2026: I am serving on the organizing committee for the SIAM Annual Meeting 2026 as SIAG CSE representative.
October 2025: New preprint on arXiv on likelihood-informed model reduction for Bayesian inference of static structural loads. This paper is led by TU Munich PhD student Jakob Scheffels, and is a product of our collaboration with TU Munich supported by my Hans Fischer Fellowship.
July 2025: I'm pleased to be the recipient of an NSF CAREER award from the program on Engineering Design and Systems Engineering (EDSE). This award will support my group's research developing machine learning methods that learn from multifidelity data.