Overview
Kyle Bradbury is the Managing Director of the Energy Data Analytics Lab at the Duke University Energy Initiative. He brings experience in machine learning and statistical modeling to energy problems. He completed his Ph.D. at Duke University, with research focused on modeling the reliability and cost trade-offs of energy storage systems for integrating wind and solar power into the grid. Kyle holds a M.S. in Electrical Engineering from Duke University where he specialized in statistical signal processing and machine learning, and a B.S. in Electrical Engineering from Tufts University. He has worked for ISO New England, MIT Lincoln Laboratories, and Dominion.
Current Duke Appointments & Affiliations
Assistant Research Professor in the Department of Electrical and Computer Engineering
·
2020 - Present
Pierre R. Lamond Department of Electrical and Computer Engineering,
Pratt School of Engineering
Assistant Research Professor in the Division of Environmental Social Systems
·
2024 - Present
Environmental Social Systems,
Nicholas School of the Environment
Faculty Fellow in the Nicholas Institute for Energy, Environment & Sustainability
·
2022 - Present
Nicholas Institute for Energy, Environment & Sustainability,
University Institutes and Centers
Recent Scholarly Works
When Centroids Mislead: Quantifying the Consequences of Sub-Optimally Aggregating Gridded Raster Data to Polygons
Journal article ISPRS International Journal of Geo Information · June 1, 2026 Point data, such as population, disease incidence, and greenhouse gas emissions, are commonly aggregated to a uniform grid of raster data for storage and representation. In many remote sensing applications, polygons are instead used to describe regions of ... Full text CiteMachine learning inversion of interatomic force constants from single-crystal inelastic neutron scattering
Journal article Digital Discovery · April 1, 2026 Atomic vibrations govern many macroscopic properties of materials, but experiments to comprehensively probe them remain challenging. Inelastic neutron scattering (INS) is a powerful technique to map phonon dispersions in crystals, especially when leveragin ... Full text CiteSegment anything, from space?
Conference Proceedings 2024 IEEE Winter Conference on Applications of Computer Vision Wacv 2024 · January 3, 2024 Recently, the first foundation model developed specifically for image segmentation tasks was developed, termed the "Segment Anything Model"(SAM). SAM can segment objects in input imagery based on cheap input prompts, such as one (or more) points, a boundin ... Full text CiteRecent Grants
IUCRC Phase I Duke University: Center for Innovation in Risk-analysis for Climate Adaption and Decision-making (CIRCAD)
ResearchParticipating Faculty Member · Awarded by National Science Foundation · 2025 - 2031Climate TRACE Phase 8
ResearchPrincipal Investigator · Awarded by WattTime · 2026 - 2027Climate TRACE Phase 7
ResearchPrincipal Investigator · Awarded by WattTime · 2025 - 2026View All Grants
Education
Duke University ·
2013
Ph.D.