Elizabeth Qian

Elizabeth Qian is an Assistant Professor at Georgia Institute of Technology, holding a joint appointment in the Schools of Computational Science and Engineering and Aerospace Engineering within the College of Computing. Her research focuses on developing computational methods for engineering design and decision-making, emphasizing model reduction, scientific machine learning, and multi-fidelity approaches. She earned her SB, SM, and PhD from MIT's Department of Aeronautics & Astronautics and held a postdoctoral position at Caltech. Qian is a Hans Fischer Fellow at TU Munich and has received numerous awards, including the SIAM Student Paper Prize and NSF Graduate Fellowship.

• Education: PhD (2017), SM (2014), SB (2013) in Aeronautics & Astronautics, MIT
• Research Interests: Model reduction for Bayesian inference, physics-informed machine learning, multi-fidelity uncertainty quantification, and surrogate models for engineering systems.

Her work bridges theory and application, with contributions to PDE-constrained optimization, ensemble Kalman inversion, and scalable computational methods. Recent publications highlight advancements in multifidelity linear regression and inference-oriented model reduction. She actively mentors students and leads the Aerospace Computational Engineering (ACE) Group at Georgia Tech.

• Awards: 2023 Hans Fischer Fellowship, NAE Frontiers of Engineering Symposium (2023), World Congress Best Presentation (2022), Caltech Teaching Award (2022)
• Advising & Grants: Supervises PhD, master’s, and undergraduate researchers; collaborates on grants involving TU Munich and NASA applications. ACE Group focuses on scalable computational methods for design and control.

Qian also serves as a guest editor for special issues on model reduction and digital twins, furthering interdisciplinary impact in computational science and engineering.