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
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. ACE Group students Tomoki Koike and Pavlos Stavrinides will also attend and present their work.
October 2024: ACE Group will have a strong presence at the SIAM Conference on Mathematics of Data Science in Atlanta, GA, October 21-25, including talks by me, Tomoki Koike, and Pavlos Stavrinides and posters presented by me, Dayoung Kang, and Tomoki. As a member of the SIAM activity group on Equity, Diversity, and Inclusion (EDI), I am also co-organizing a session with Tammy Kolda on Mathematical and Statistical Methods for Promoting Fairness and Equity in Algorithmic Decision-making on Friday afternoon.
Recent news
August 2024: A warm welcome to several new group members: GT PhD students Atticus Rex and Weiting Yi, Fulbright visiting student Josie König, and GT undergraduate researchers Kashvi Mundra and Andy Yu!
I presented our recent work on multifidelity machine learning from scarce data (i) in the Data-Driven Physical Simulation (DDPS) webinar series hosted by Lawrence Livermore National Laboratory on August 2, and (ii) at UQ-MLIP 2024, the second USACM thematic conference on uncertainty quantification for machine learning integrated physics modeling, from August 12-14.
July 2024: An update to our preprint on a new multifidelity machine learning approach to learning from scarce data is available on arXiv. The work learns more accurate and robust models by combining high- and low-fidelity data. This work is a collaboration between myself and PhD student Dayoung Kang with Anirban Chaudhuri and Vignesh Sella from UT Austin.