Natalie Maus
PhD Candidate, ABD (All But Dissertation)
University of Pennsylvania, Computer and Information Science Department
Advised by: Professor Jacob R. Gardner
Funded by: NSF GRFP (Awarded in 2023)
Email: nmaus@seas.upenn.edu
Links to GitHub, Google Scholar
Research Interests: I am interested in probabilistic machine learning, Bayesian optimization, and generative modeling. I am particularly interested in developing and applying Bayesian optimization and generative modeling techniques to solve challenging design problems in the natural sciences. In my work, I have applied these techniques to design new antibiotics, antibodies, RNA sequences, superconducting materials, and more. Recently, our work on antimicrobial peptide design led to the successful discovery of new and more useful antibiotics, and the first in vivo experimental validation of generative Bayesian optimization in any setting [see paper].
