Program

Dr. Hannah Kerner

Keynote: Flexible, multi-modal foundation models for satellite Earth observations

Bio: Hannah Kerner is an Assistant Professor in the School of Computing and Augmented Intelligence at Arizona State University. Her research focuses on advancing the foundations and applications of machine learning to foster a more sustainable, responsible, and fair future for all. As the AI Lead for NASA’s agriculture programs, NASA Harvest and NASA Acres, she is deploying research methods in real applications across the globe; her projects have directly resulted in optimized agricultural planning, disaster response, and financial relief in various regions around the world. The impact of Kerner’s research was recognized in Forbes 30 Under 30 and the International Research Centre On Artificial Intelligence’s Top 10 projects solving problems related to the UN’s Sustainable Development Goals with AI.

For more: https://hannah-rae.github.io/

Dr. Nadia Shakoor

Keynote: The transformative role of geospatial technologies in agriculture: how advanced sensing and AI-driven data analysis are reshaping crop improvement, management and sustainability efforts.

Bio: Dr. Shakoor is an Assistant Member and Principal Investigator at the Donald Danforth Plant Science Center, as well as the co-founder and CEO of Agrela Ecosystems. Nadia’s career has centered on integrating cutting-edge geospatial and phenotyping technologies to advance agricultural practices. At the Danforth Center, she has been a driving force behind several high-impact projects, including the Bill and Melinda Gates-funded Sorghum Genomics Toolbox, which developed genomic and phenotyping tools for sorghum breeding, and the ARPA-E funded TERRA-REF project, deploying the world’s largest field crop analytics robot in Maricopa, Arizona. As the founder of Agrela Ecosystems, Nadia has developed the PheNode, a core sensor platform that gathers critical agricultural data, and leads the USDA-funded FieldDock project, which integrates autonomous drones and wireless sensor networks for enhanced field monitoring. She is also collaborating with the Salk Institute for Biological Studies to leverage geospatial sensing technologies for optimizing carbon capture and sequestration in sorghum. Additionally, Nadia is working with the National Sorghum Producers on their USDA-funded Climate Smart-Commodities program, advancing climate-smart sorghum genetics and field management practices.

For more: https://www.danforthcenter.org/our-work/principal-investigators/nadia-shakoor/

Dr. Caleb Robinson

Panel Speaker

Bio: Dr. Robinson is a Principal Research Science Manager in the Microsoft AI for Good Research Lab(opens in new tab). He graduated from the Georgia Institute of Technology with a PhD in 2020 and his work focuses on tackling large scale problems at the intersection of remote sensing and machine learning/computer vision. At the AI for Good Lab he co-leads the Geospatial ML research group(opens in new tab) and is the lead researcher on the Global Renewables Watch(opens in new tab), rapid damage assessment, and global building density estimation teams. Caleb is interested in research topics that facilitate using remotely sensed imagery more effectively in conservation, sustainability, and damage response application. For example: self-supervised methods for training deep learning models with large amounts of unlabeled satellite imagery, human-in-the-loop methods for creating and validating modeled layers, and domain adaptation methods for developing models that can generalize over space and time.

Dr.-Ing. Ribana Roscher

Panel Speaker

Bio: Dr. Roscher is a Professor of Data Science for Crop Systems at the University of Bonn, Germany. She heads the same-titled group at the Institute of Bio- and Geosciences at Forschungszentrum Jülich, Germany. She develops machine learning methods to address challenges in environmental sciences and sustainable agriculture. She specifically focuses on techniques for sophisticated feature learning, data-centric, and explainable machine learning.