Scholar
Hannah Rae Kerner
Google Scholar ID: g5CD7dQAAAAJ
Assistant Professor, Arizona State University
Citations & Impact
Citations
1,404
H-index
21
i10-index
38
Publications
20
Co-authors
6
list available
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- NSF CAREER award (2025), Top 10 of 100 projects solving problems related to the UN SDGs with AI by International Research Centre on Artificial Intelligence (IRCAI) for NASA Harvest (2021), Forbes 30 Under 30 in Science (2021), Outstanding Research Faculty department award (2021), Radiant Earth Foundation’s 15 Leading Women in ML4EO (2021), Google Women Techmakers award (2018). Multiple papers accepted and published in top conferences like ICML and CVPR.
Research Experience
- Prior to joining the faculty at ASU, I was an Assistant Research Professor at the University of Maryland, College Park. I have worked at NASA’s Jet Propulsion Laboratory, Goddard Space Flight Center, and Langley Research Center, as well as the commercial remote sensing company Planet, Inc.
Education
- Ph.D. from Arizona State University on machine learning methods (especially novelty detection) for planetary exploration missions. B.S. in Computer Science from the University of North Carolina at Chapel Hill where I conducted research on 3D motion planning for autonomous agents.
Background
- Currently an Assistant Professor in the School of Computing and Augmented Intelligence at Arizona State University. My research focuses on advancing the foundations and applications of machine learning to foster a more sustainable, responsible, and fair future for all. Our lab is conducting research projects in machine learning for remote sensing, algorithmic bias, and machine learning theory. I am the AI/Machine Learning Lead for NASA Harvest and NASA Acres as well as Center Faculty for the ASU Center for Global Discovery and Conservation Science (GDCS).
Miscellany
- Passionate about advancing opportunities for people who have traditionally been underrepresented in or excluded from computer science and devote much of my time outside of research to these efforts.
