Elizabeth Qian
Elizabeth Qian holds a joint appointment at Georgia Tech as an Assistant Professor in the Schools of Aerospace Engineering and Computational Science and Engineering. Her interdisciplinary research develops new computational methods to enable engineering design and decision-making for complex systems. Her specialties are in developing efficient surrogate models through model reduction and scientific machine learning, and in developing multifidelity approaches to accelerate expensive computations in uncertainty quantification, optimization, and control.
Qian previously held a postdoctoral appointment as von Karman Instructor at Caltech in the Department of Computing + Mathematical Sciences. She has been the recipient of many awards, including a 2025 NSF Faculty Early Career Development award, a 2024 Air Force Office of Scientific Research Young Investigator Program award, a 2022 Associated Students of the California Institute of Technology teaching award, the 2020 SIAM Student Paper Prize, the Fannie and John Hertz Foundation Fellowship, and the NSF Graduate Research Fellowship. She is also an alumna of the U.S. Fulbright student program. She earned her PhD, SM, and SB degrees from the MIT Department of Aeronautics & Astronautics, and currently holds a visiting appointment as a Hans Fischer Fellow at the Technical University of Munich.
Professor Qian’s teaching interests focus on training engineering students in computational methods and theory, enabling engineers to fully leverage modern computing capabilities. Professor Qian is committed to fostering collaborative and interdisciplinary learning environments for both undergraduate and graduate students.
Professor Qian’s research centers on the development of computational methods and theory to support engineering decision-making. The research integrates applied math, algorithmic development, and engineering applications. Graduate and undergraduate students are actively engaged in her projects.
Lab/Collaborations/Groups:
Disciplines:
- Flight Mechanics & Controls
- Numerical methods and analysis
- Systems Design & Optimization
- Computational science and engineering
- Scientific machine learning
AE Multidisciplinary Research Areas:
- Large-Scale Computations, Data, and Analytics
- PhD Aerospace Computational Engineering, MIT, 2021;
- MS Aerospace Engineering, MIT 2017;
- BS Aerospace Engineering, MIT 2014.
o NSF CAREER 2025 o Air Force Office of Scientific Research Young Investigator Program 2024
o Technical University of Munich Institute for Advanced Study Hans Fischer Fellowship 2023
o Associated Students of the California Institute of Technology Teaching Award 2022
o Caltech Division of Engineering and Applied Sciences New Horizons Diversity, Equity, and Inclusion Award 2022
o National Science Foundation Graduate Research Fellowship 2014
o Fannie and John Hertz Foundation Fellowship 2014
o Fulbright Student Grant 2014
- T Koike, E Qian, Physics-Informed Machine Learning for Characterizing System Stability, AIAA SCITECH 2026 Forum, 1617, 2026
- T Koike, P Mohan, MTH de Frahan, J Bessac, E Qian, Streaming Operator Inference for Model Reduction of Large-Scale Dynamical Systems, arXiv preprint arXiv:2601.12161, 2026
- P Stavrinides, E Qian, An ensemble Kalman approach to randomized maximum likelihood estimation, arXiv preprint arXiv:2507.03207, 2025
- J Scheffels, E Qian, I Papaioannou, E Ullmann, Likelihood-informed Model Reduction for Bayesian Inference of Static Structural Loads, arXiv preprint arXiv:2510.07950, 2025
- E Qian, C Beattie, The fundamental subspaces of ensemble Kalman inversion, SIAM Review 67 (4), 771-798, 2025