Daniel Adams
Associate Research & Development Scientist / Geospatial Scientist
Bio
Daniel is an Associate R&D Scientist with the Human Geography group at 91°µÍø (ORNL). His research interests lie at the intersection of spatial data mining, inherent randomness, and structured decision-making in geospatial sciences. He frequently applies probabilistic and machine learning techniques, including Bayesian reasoning and deep learning, to domains such as human dynamics, built environment characterization, climate security, and landscape ecology. Daniel is passionate about turning spatial uncertainty into actionable insight for real-world challenges. He holds a Ph.D. from Tennessee Technological University and currently leads the LandScan HD and LandScan Mosaic projects, advancing high-resolution population modeling.
Awards
2022 Tennessee Technological University Creative Research and Inquiry Day - Doctoral Research Award in Computer Science
data:
2021 U.S. Fish & Wildlife Service - Southeast Region Director's Honor Award
Publications
Chapter "GeoAI for Humanitarian Assistance" in Book "Handbook of Geospatial Artificial Intelligence"
Other Publications
Ecological Correlates of Reproductive Output in a Tennessee Population of Short's Bladderpod, Physaria globosa (Brassicaceae)
DOI: 10.2179/0008-7475.87.1.20
Science needs of southeastern grassland species of conservation concern: A framework for species status assessments
DOI: 10.3133/ofr20211047