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. 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.
Upcoming talks & activities
Spring 2025 semester: I will teach two different special topics courses, AE 4803 AIM on scientific machine learning (undergraduate level) and AE 4803/8803 NUM/QNU on numerical methods (joint undergrad/grad). For more information, see my teaching page. Georgia Tech students should feel free to email with questions about either course.
January 2025: I will be at AIAA SciTech Jan 7-9, chairing MDO-15, NDA-04/MDO-18, and NDA-08. I'll also attend the MDO and NDA Technical Committee meetings.
I will attend the IPAM Workshop on Sampling, Inference, and Data-Driven Physical Modeling in Scientific Machine Learning January 13-17 (I'll be there in the latter half of the week due to teaching duties).
Recent news
November 2024: Our work on Multifidelity linear regression for scientific machine learning from scarce data has been published in Foundations of Data Science and is free to access under FoDS Early Access at the moment.
September 2024: New preprint on The Fundamental Subspaces of Ensemble Kalman Inversion (EKI) is online. EKI methods are a family of adjoint/derivative-free iterative methods for solving least-squares problems. This joint work with Christopher Beattie (Virginia Tech) provides a new analysis of EKI for solving linear least-squares problems that illuminates six fundamental subspaces of EKI analogous to the famous four fundamental subspaces of linear algebra (à la Strang).