David Makowski, Ph.D.
French National Institute for Agriculture, Food, and Environment | INRAE
His interests include statistical/machine learning modeling applied to agroecology, climate change, and food safety. He manages scientific projects and supports research groups analyzing complex datasets using statistical and machine learning methods. He is focused on statistical modeling to assess the impact of climate on agricultural production and to design resilient cropping systems.
Maia Donahue, Ph.D.
At Corteva, Maia is the Digital Farming Lead in the Farming Solutions & Digital department within Research & Development. As the Digital Farming Lead, Maia and her team are responsible for establishing the priorities and directions of Corteva’s external-facing digital solutions, supporting our customers and sales channels with data science and agronomy-backed recommendations to provide the most value for Corteva’s Seeds and Crop Protection products. Maia has spent her entire Corteva career within Research & Development. Prior to her current role, Maia led a team of software & data engineers delivering core geospatial data & software capabilities to support predictive analytics. Prior to this, Maia was a Systems Biology Technical Leader, supporting the development of new traits and crop protection molecules through applications of mathematical modeling with early discovery research teams.
Girish Chowdhary, Ph.D.
University of Illinois Urbana-Champaign
He is a computer science specialist at the University of Illinois. His academic career and interest in robotics come with a real-world orientation, so he turned his expertise in automation to reducing the uncomfortable, labour-intensive jobs in agriculture.
The company Girish co-founded, EarthSense, developed its technology to meet the needs of plant breeders, creating robots that can quickly, accurately, and autonomously collect phenotyping data in plot trials.
Hannah Kerner, Ph.D.
Arizona State University
She is an assistant professor in the School of Computing and Augmented Intelligence. Her research focuses on developing AI and machine learning advances needed to address the world’s most pressing challenges, including food security, climate change and space exploration. As the AI lead for the NASA Harvest program, she is deploying research methods in real operations for stakeholders in industry, government and humanitarian organizations.
Michael Humber, Ph.D.
NASA Acres -
University of Maryland
He is an Associate Research Professor and Instructor who has been at the University of Maryland since Fall 2007. He started research with the Department of Geographical Sciences in Fall 2010 as an undergraduate intern. Initially, his work was focused on photointerpreting wildfire burn scars in Landsat images to create a validation dataset for the MODIS Collection 5.1 MCD45A1 Burned Area product. During this time, he developed the workflows for generating vector files from the MCD45A1 Burned Area product which are distributed to the public and contributed to developing a NASA World Wind Java-based 3-D visualization tool for the MODIS Burned Area and Active Fire products.