Natalie Maus
PhD Student
Department of Computer and Information Science
University of Pennsylvania
Email: nmaus@seas.upenn.edu
Links to Github, Google Scholar
Recipient of the 2023 National Science Foundation Graduate Research Fellowship (NSF GRFP)
Notable Paper Award and Oral Presentaiton at the 2023 Artificial Intelligence and Statistics conference (AISTATS 2023)
About me
I am a PhD student in computer and information science at the University of Pennsylvania, advised by Professor Jacob Gardner. I am interested in machine learning, Bayesian optimization, generative modeling, and applications in computational drug design. Previously, I completed my bachelor's degree double-majoring in computer science and physics at the Colby College in Waterville, Maine.
Publications
Maus N, Chao P, Gardner J, Wong E. Black Box Adversarial Prompting for Foundation Models. Submitted for publication to SATML 2024 (TBD) [paper] [blog post]
Zhu X, Wu K, Maus N, Gardner J, Bindel D. Variational Gaussian Processes with Decoupled conditionals. NeurIPS. (2023) [paper]
Maus N, Wu K, Eriksson D, Gardner J. Discovering Many Diverse Solutions with Bayesian Optimization. AISTATS. (2023) [paper] Selected for Oral Presentation, Notable Paper Award
Maus N, Jones H, Moore J, Kusner J, Bradshaw J, Gardner J. Local Latent Space Bayesian Optimization over Structured Inputs. NeurIPS. (2023) [paper]
Maus N, Layton OW. Estimating Heading from Optic Flow: Comparing Deep Learning Network and Human Performance. Neural Netw. (2022) [paper]
Verkhoglyadova O, Maus N, Meng X. Classification of High Density Regions in Global Ionospheric Maps with Neural Networks. Earth and space science. (2021) [paper]
Maus N, Rutledge D, Al-Khazraji S, Bailey R, Ovesdotter Alm C, Shinohara K. Gaze-guided Magnification for Individuals with Vision Impairments. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (CHI). (2020). [paper]