Hannah Kerner

Hannah Kerner is an American data scientist and research scientist specializing in the application of artificial intelligence and machine learning to challenges in Earth science, agriculture, and food security. Her work primarily involves developing algorithms to analyze satellite imagery and other geospatial data for monitoring crop health, predicting yields, and mapping land use. Kerner has held significant roles at NASA and within academia, contributing to high-profile projects like NASA Harvest, the agency's food security and agriculture consortium.

Early life and education

Hannah Kerner completed her undergraduate studies at the University of Arizona, where she earned a Bachelor of Science in systems engineering. She then pursued graduate studies at the University of Maryland, College Park, a leading institution in remote sensing and geographical information science. At Maryland, she was part of the Goddard Space Flight Center's research community, earning both a Master of Science and a Doctor of Philosophy in computer science. Her doctoral research focused on machine learning techniques for analyzing data from Earth observation satellites, laying the foundation for her subsequent career at the intersection of AI and Earth system science.

Career and research

Following her PhD, Kerner joined NASA's Ames Research Center in Silicon Valley as a research scientist. There, she became heavily involved with NASA Harvest, serving as the AI Lead and later as the Director of AI and Autonomy. In this capacity, she led efforts to develop and deploy machine learning models that utilize data from missions like Landsat, Sentinel-2, and the MODIS instrument to generate actionable insights for agricultural monitoring globally. Her research has addressed critical issues such as crop type classification, drought detection, and yield forecasting in regions including Sub-Saharan Africa and Ukraine. In 2023, she transitioned to a faculty position as an Assistant Professor in the School of Computing and Augmented Intelligence at Arizona State University. At ASU, her research group continues to advance applied machine learning for sustainability and disaster response, collaborating with organizations like the U.S. Department of Agriculture and the Group on Earth Observations.

Awards and recognition

Kerner's innovative work has been recognized through several prestigious awards. She is a recipient of the NASA Early Career Faculty Award, which supports outstanding early-career faculty who conduct foundational research aligned with NASA's mission. She has also received the NASA Ames Honor Award for her exceptional contributions to the NASA Harvest program. Her research publications are frequently presented at top-tier conferences including the NeurIPS, the ICML, and the IGARSS.

Selected publications

Kerner's scholarly output includes numerous peer-reviewed articles and conference papers. Notable publications include studies on self-supervised learning for crop classification in *Remote Sensing of Environment*, transfer learning for yield prediction in *Environmental Research Letters*, and the application of convolutional neural networks to synthetic-aperture radar data in the proceedings of the CVPR. She has also co-authored influential white papers and reports for the Food and Agriculture Organization of the United Nations on using satellite data for agricultural statistics.

Personal life

Based in Tempe, Arizona, Hannah Kerner is actively involved in the broader scientific community as a mentor and advocate for open science. She frequently participates in initiatives aimed at increasing accessibility to AI tools and Earth observation data for researchers and practitioners in developing countries. Outside of her professional work, she has expressed interests in hiking, photography, and science communication.

Category:American computer scientists Category:American earth scientists Category:21st-century American women scientists Category:University of Maryland, College Park alumni Category:Arizona State University faculty Category:NASA people