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 of the Duke University Energy Initiative
  • Faculty Fellow in The Energy Initiative

Contact Information

Education

  • Ph.D. Duke University, 2013

Courses Taught

  • ENERGY 395: Connections in Energy: Interdisciplinary Team Projects
  • ENERGY 395T: Bass Connections Energy & Environment Research Team
  • ENERGY 396: Connections in Energy: Interdisciplinary Team Projects
  • ENERGY 396T: Bass Connections Energy & Environment Research Team
  • ENERGY 795: Connections in Energy: Interdisciplinary Team Projects
  • ENERGY 795T: Bass Connections Energy & Environment Research Team
  • ENERGY 796: Connections in Energy: Interdisciplinary Team Projects
  • ENERGY 796T: Bass Connections Energy & Environment Research Team
  • IDS 705: Principles of Machine Learning

Representative Publications

  • Ren, S., J. Malof, R. Fetter, R. Beach, J. Rineer, and K. Bradbury. “Utilizing Geospatial Data for Assessing Energy Security: Mapping Small Solar Home Systems Using Unmanned Aerial Vehicles and Deep Learning.” Isprs International Journal of Geo Information 11, no. 4 (April 1, 2022). https://doi.org/10.3390/ijgi11040222.
  • Xu, Y., B. Huang, X. Luo, K. Bradbury, and J. M. Malof. “SIMPL: Generating Synthetic Overhead Imagery to Address Custom Zero-shot and Few-Shot Detection Problems.” Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, January 1, 2022. https://doi.org/10.1109/JSTARS.2022.3172243.
  • Hu, Wei, Bohao Huang, Kyle Bradbury, Jordan Malof, Varun Nair, Tamasha Pathirathna, Xiaolan You, et al. “Electric Transmission Infrastructure Satellite Imagery Dataset for Computer Vision,” July 21, 2021. https://doi.org/10.6084/m9.figshare.14935434.v2.
  • Huang, B., J. Yang, A. Streltsov, K. Bradbury, L. M. Collins, and J. Malof. “GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery.” Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, January 1, 2021. https://doi.org/10.1109/JSTARS.2021.3124519.
  • Hu, W., T. Feldman, Y. J. Ou, N. Tarn, B. Ye, Y. Xu, J. M. Malof, and K. Bradbury. “Wind Turbine Detection with Synthetic Overhead Imagery.” In International Geoscience and Remote Sensing Symposium (Igarss), 4908–11, 2021. https://doi.org/10.1109/IGARSS47720.2021.9554306.