Scholar
Natalie Maus
Google Scholar ID: hNRd6lsAAAAJ
PhD. Student, University of Pennsylvania Department of Computer and Information Science
Citations & Impact
Citations
336
H-index
6
i10-index
6
Publications
20
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- - Local Latent Space Bayesian Optimization over Structured Inputs, NeurIPS 2022
- - A Generative Artificial Intelligence Approach for Antibiotic Optimization, Nature Biotechnology 2025 (Under Review)
- - A Dataset for Distilling Knowledge Priors from Literature for Therapeutic Design, NeurIPS 2025
- - Discovering Many Diverse Solutions with Bayesian Optimization, AISTATS 2023 Notable Paper
- - Approximation-Aware Bayesian Optimization, NeurIPS 2024 Spotlight
- - Covering Multiple Objectives with a Small Set of Solutions Using Bayesian Optimization, NeurIPS 2025
- - Generative Modeling for RNA Splicing Predictions and Design, eLife 2025
- - Learned Offline Query Planning via Bayesian Optimization, SIGMOD 2025
- - Joint Composite Latent Space Bayesian Optimization, ICML 2024
- - Invited Plenary Speaker, French Research Network on Uncertainty Quantification (RT-UQ) annual meeting - 2026
- - Invited Speaker, Meta Adaptive Experimentation Workshop – 2025, 2024
- - Invited Speaker, IMSI Workshop on Kernel Methods in Uncertainty Quantification and Experimental Design – 2025
- - Invited Guest Lecturer, Columbia University PhD-level course on Bayesian optimization – 2024
- - Invited Speaker, SIAM Conference on Uncertainty Quantification (SIAM UQ24) – 2024
- - Invited Speaker, SIAM Conference on Computational Science and Engineering (SIAM CSE23) – 2023
- - Notable Paper Award & Oral Presentation, Artificial Intelligence and Statistics conference (AISTATS) – 2023
- - Fellowship Award, National Science Foundation Graduate Research Fellowship Program (NSF GRFP) – 2023
Research Experience
- - ML Intern at BigHat Biosciences, developing structure prediction methods for antibody therapeutic design (Summer 2024)
- - Research Intern at Meta, Central Applied Science Team, developing Bayesian optimization methods; first-author ICML 2024 paper (Summer 2023)
- - Research Intern at NASA Jet Propulsion Laboratory, applying ML to ionospheric data; co-authored ESS 2021 paper (Summer 2020)
Education
- PhD Candidate, University of Pennsylvania, Computer and Information Science Department, advised by Professor Jacob R. Gardner, funded by NSF GRFP (Awarded in 2023)
Background
- Research interests include probabilistic machine learning, Bayesian optimization, and generative modeling, with a particular focus on applying these techniques to design problems in the natural sciences. In her work, she has applied these techniques to design new antibiotics, antibodies, RNA sequences, superconducting materials, and more.
Miscellany
- - Co-organizer, Virtual Seminar Series on Bayesian Decision-making and Uncertainty (2025)
- - Co-organizer, SIAM CSE Minisymposium on Uncertainty Quantification in Scientific Machine Learning (2025)
- - Co-organizer, NeurIPS Workshop on Bayesian Decision-making and Uncertainty (2024)
Co-authors