Natalie Maus

PhD Candidate (Expected Graduation: May 2026)

University of Pennsylvania, Computer and Information Science Department

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 vitro and in vivo experimental validation of generative Bayesian optimization in any setting [see paper].

Publications

Tao J, Maus N, Jones HT, Zeng Y, Gardner JR, Marcus R. Learned Offline Query Planning via Bayesian Optimization. SIGMOD. (2025) [paper]
Wu D, Maus N, Jha A, Yang K, Wales-McGrath BD, Jewell S, Tangiyan A, Choi P, Gardner JR, Barash Y. Generative modeling for RNA splicing predictions and design. eLife. (2025) [paper]
Torres MDT, Zeng Y, Wan F, Maus N, Gardner JR, de la Fuente-Nunez C. A generative artificial intelligence approach for antibiotic optimization. Paper submission under review. (2024) [paper]
Maus N, Kim K, Pleiss G, Eriksson D, Cunningham JP, Gardner JR. Approximation-Aware Bayesian Optimization. NeurIPS. (2024) [paper] NeurIPS Spotlight
Maus N, Chao P, Gardner JR, Wong E. Black Box Adversarial Prompting for Foundation Models. (2024) [paper] [blog post]
Maus N, Lin ZJ, Balandat M, Bakshy E. Joint Composite Latent Space Bayesian Optimization. ICML. (2024) [paper]
Zhu X, Wu K, Maus N, Gardner JR, Bindel D. Variational Gaussian Processes with Decoupled conditionals. NeurIPS. (2023) [paper]
Maus N, Zeng Y, Anderson DA, Maffettone P, Solomon A, Greenside P, Bastani O, Gardner JR. Inverse Protein Folding Using Deep Bayesian Optimization. (2023) [paper]
Maus N, Wu K, Eriksson D, Gardner JR. Discovering Many Diverse Solutions with Bayesian Optimization. AISTATS. (2023) [paper] AISTATS Oral, 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]

Awards and Invited Talks

Invited speaker at the 2025 IMSI Kernel Methods in Uncertainty Quantification and Experimental Design workshop
Invited speaker at the 2024 Meta Adaptive Experimentation Workshop
Invited speaker at the 2024 SIAM Conference on Uncertainty Quantification (SIAM UQ24)
Invited speaker at the 2023 SIAM Conference on Computational Science and Engineering (SIAM CSE23)
Notable Paper Award and Oral Presentation at the 2023 Artificial Intelligence and Statistics conference (AISTATS 2023)
Recipient of the 2023 National Science Foundation Graduate Research Fellowship (NSF GRFP)

Organizational Roles

Co-organizer of the Uncertainty Quantification in Scientific Machine Learning Minisymposium at the 2025 SIAM Conference on Computational Science & Engineering (SIAM CSE25)
Co-organizer of the Bayesian Decision-making and Uncertainty workshop at the 2024 Conference on Neural Information Processing Systems (NeurIPS 2024)
Co-organizer of the Virtual Seminar Series on Bayesian Decision-making and Uncertainty