Kyle Bradbury

Assistant Research Professor in the Department of Electrical and Computer Engineering

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.

Appointments and Affiliations

  • Assistant Research Professor in the Department of Electrical and Computer Engineering
  • Managing Director, Energy Data Analytics Lab, Nicholas Institute for Energy, Environment & Sustainability
  • Faculty Fellow in the Nicholas Institute for Energy, Environment & Sustainability

Contact Information

Education

  • Ph.D. Duke University, 2013

Awards, Honors, and Distinctions

  • Bass Connections Award for Outstanding Leadership. Duke University. 2022

Courses Taught

  • IDS 794: Independent Study
  • IDS 705: Principles of Machine Learning
  • HOUSECS 59: House Course
  • ENERGY 796T: Bass Connections Energy & Environment Research Team
  • ENERGY 795T: Bass Connections Energy & Environment Research Team
  • ENERGY 795: Connections in Energy: Interdisciplinary Team Projects
  • ENERGY 396T: Bass Connections Energy & Environment Research Team
  • ENERGY 395T: Bass Connections Energy & Environment Research Team
  • EGR 393: Research Projects in Engineering
  • ECE 494: Projects in Electrical and Computer Engineering
  • ECE 392: Projects in Electrical and Computer Engineering

Representative Publications

  • Robinson, Celine, Kyle Bradbury, and Mark E. Borsuk. “Remotely sensed above-ground storage tank dataset for object detection and infrastructure assessment.” Scientific Data 11, no. 1 (January 2024): 67. https://doi.org/10.1038/s41597-023-02780-1.
  • Yaras, C., K. Kassaw, B. Huang, K. Bradbury, and J. M. Malof. “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 17 (January 1, 2024): 1988–98. https://doi.org/10.1109/JSTARS.2023.3340412.
  • Luzi, F., A. Gupta, L. Collins, K. Bradbury, and J. Malof. “Transformers For Recognition In Overhead Imagery: A Reality Check.” In Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023, 3767–76, 2023. https://doi.org/10.1109/WACV56688.2023.00377.
  • Kornfein, Caleb, Frank Willard, Caroline Tang, Yuxi Long, Saksham Jain, Jordan Malof, Simiao Ren, and Kyle Bradbury. “Closing the domain gap: blended synthetic imagery for climate object detection.” Environmental Data Science 2 (2023). https://doi.org/10.1017/eds.2023.33.
  • Hu, W., K. Bradbury, J. M. Malof, B. Li, B. Huang, A. Streltsov, K. Sydny Fujita, and B. Hoen. “What you get is not always what you see—pitfalls in solar array assessment using overhead imagery.” Applied Energy 327 (December 1, 2022). https://doi.org/10.1016/j.apenergy.2022.120143.