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Kyle Bradbury

Assistant Research Professor in the Department of Electrical and Computer Engineering
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 Appointments & Affiliations


Assistant Research Professor in the Department of Electrical and Computer Engineering · 2020 - Present 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

In the News


Published February 7, 2025
Duke Experts Provide Clearest Picture Yet of Global Building Emissions

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Recent Publications


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

Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery

Journal Article IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · January 1, 2024 Modern deep neural networks (DNNs) are highly accurate on many recognition tasks for overhead (e.g., satellite) imagery. However, visual domain shifts (e.g., statistical changes due to geography, sensor, or atmospheric conditions) remain a challenge, causi ... Full text Cite

Remotely sensed above-ground storage tank dataset for object detection and infrastructure assessment.

Journal Article Scientific data · January 2024 Remotely sensed imagery has increased dramatically in quantity and public availability. However, automated, large-scale analysis of such imagery is hindered by a lack of the annotations necessary to train and test machine learning algorithms. In this study ... 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 - 2030

Climate TRACE Phase 7

ResearchPrincipal Investigator · Awarded by WattTime · 2025 - 2026

Climate TRACE Phase 6

ResearchPrincipal Investigator · Awarded by WattTime · 2024 - 2025

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Education, Training & Certifications


Duke University · 2013 Ph.D.