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
April 2024: I will give the CODES Seminar at Emory University on April 4, presenting our new paper on multifidelity linear regression.
PhD student Pavlos Stavrinides will present at the upcoming Copper Mountain Conference on Iterative Methods, April 14-19, at Copper Mountain, CO.
May 2024: PhD student Pavlos Stavrinides will give a talk at the SIAM Conference on Imaging Sciences, May 28-31, in Atlanta, GA.
August 2024: I will present at UQ-MLIP 2024, the second USACM thematic conference on uncertainty quantification for machine learning integrated physics modeling, from August 12-14.
September 2024: I am serving on the scientific committee for MORe2024, an excellent workshop on model reduction and surrogate modeling that will be held in San Diego from September 9-13.
Recent news
March 2024: Our preprint on a new multifidelity machine learning approach to learning from scarce data is available on arXiv. The work exploits the structure of linear regression problems to learn models from scientific and engineering data in a more robust way, leading to more accurate learned models when training data are scarce. This work is a collaboration between myself and PhD student Dayoung Kang with Anirban Chaudhuri and Vignesh Sella from UT Austin.
February 2024: I attended the SIAM Conference on Uncertainty Quantification held in Trieste, Italy from February 27 to March 1.