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Kyle Bradbury

Assistant Research Professor in the Department of Electrical and Computer Engineering
Pierre R. Lamond Department of Electrical and Computer Engineering
140 Science Drive (Gross Hall), Box 90467, Durham, NC 27708

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 News Items


Published February 7, 2025
Nicholas Institute for Energy, Environment & Sustainability
Duke Experts Provide Clearest Picture Yet of Global Building Emissions

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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 Cite

Machine 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 Cite

Segment 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 Cite
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Recent 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 - 2031

Climate TRACE Phase 8

ResearchPrincipal Investigator · Awarded by WattTime · 2026 - 2027

Climate TRACE Phase 7

ResearchPrincipal Investigator · Awarded by WattTime · 2025 - 2026

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Education


Duke University · 2013 Ph.D.