Elizabeth is a final-year PhD student in the MIT Center for Computational Science and Engineering and Department of Aeronautics & Astronautics. Trained as an aerospace engineer, Elizabeth's interests lie in developing computational tools that enhance engineers' ability to make smart decisions about complex systems subject to randomness and uncertainties. Her PhD work develops a new physics-informed model learning method that fits cheap approximate models to data while respecting the structure of the governing physics. Elizabeth is committed to advancing equity in the academic enterprise and has served on the MIT Committee on Sexual Misconduct, Prevention, and Response (3y), the Graduate Women in Aerospace Engineering executive board (4.5y).
computational engineering, computational math, model reduction, multifidelity methods, uncertainty quantification