Kyle Bradbury

简介

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.

教育经历

• Ph.D. Duke University, 2013
• Assistant Research Professor in the Department of Electrical and Computer Engineering
• Assistant Research Professor in the Division of Environmental Social Systems
• Faculty Fellow in the Nicholas Institute for Energy, Environment & Sustainability

代表成果

• Markakis PJ, Malof JM, Collins L, Bradbury K. When Centroids Mislead: Quantifying the Consequences of Sub-Optimally Aggregating Gridded Raster Data to Polygons. ISPRS International Journal of Geo Information. 2026 Jun 1;15(6).
• Sable A, Linjawi B, Bradbury K, Malof J, Delaire O. Machine learning inversion of interatomic force constants from single-crystal inelastic neutron scattering. Digital Discovery. 2026 Apr 1;5(4):1545u201357.
• Ren S, Luzi F, Lahrichi S, Kassaw K, Collins LM, Bradbury K, et al. Segment anything, from space? In: Proceedings 2024 IEEE Winter Conference on Applications of Computer Vision Wacv 2024. 2024. p. 8340u201350.
• Yaras C, Kassaw K, Huang B, Bradbury K, Malof JM. Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2024 Jan 1;17:1988u201398.
• Robinson C, Bradbury K, Borsuk ME. Remotely sensed above-ground storage tank dataset for object detection and infrastructure assessment. Scientific data. 2024 Jan;11(1):67.
• Hakizimana M, Mavis E, Chiu Y, Malof J, Bradbury K. Enhanced Remote Sensing Model Performance Through Self-Supervised Learning with Multi-Spectral Data. In: International Geoscience and Remote Sensing Symposium IGARSS. 2024. p. 2833u20136.
• Kornfein C, Willard F, Tang C, Long Y, Jain S, Malof J, et al. Closing the domain gap: Blended synthetic imagery for climate object detection. Environmental Data Science. 2023 Nov 28;2.
• Luzi F, Gupta A, Collins L, Bradbury K, Malof J. Transformers For Recognition In Overhead Imagery: A Reality Check. In: Proceedings 2023 IEEE Winter Conference on Applications of Computer Vision Wacv 2023. 2023. p. 3767u201376.
• Hu W, Bradbury K, Malof JM, Li B, Huang B, Streltsov A, et al. What you get is not always what you seeu2014pitfalls in solar array assessment using overhead imagery. Applied Energy. 2022 Dec 1;327.
• Ren S, Hu W, Bradbury K, Harrison-Atlas D, Malaguzzi Valeri L, Murray B, et al. Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis. Applied Energy. 2022 Nov 15;326.