Kyle Bradbury

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

Research Interests

Solving energy problems using machine learning and statistical modeling

Courses Taught

  • ECE 391: Projects in Electrical and Computer Engineering
  • ENERGY 395: Connections in Energy: Interdisciplinary Team Projects
  • ENERGY 396: Connections in Energy: Interdisciplinary Team Projects
  • ENERGY 795: Connections in Energy: Interdisciplinary Team Projects
  • ENERGY 796: Connections in Energy: Interdisciplinary Team Projects
  • IDS 705: Principles of Machine Learning

Representative Publications

  • Streltsov, A; Malof, JM; Huang, B; Bradbury, K, Estimating residential building energy consumption using overhead imagery, Applied Energy, vol 280 (2020) [10.1016/j.apenergy.2020.116018] [abs].
  • Bradbury, K; Saboo, R; Malof, J; Johnson, T; Devarajan, A; Zhang, W; Collins, L; Newell, R; Streltsov, A; Hu, W, Distributed Solar Photovoltaic Array Location and Extent Data Set for Remote Sensing Object Identification (2020) [10.6084/m9.figshare.3385780] [abs].
  • Kong, F; Huang, B; Bradbury, K; Malof, JM, The synthinel-1 dataset: A collection of high resolution synthetic overhead imagery for building segmentation, Proceedings 2020 Ieee Winter Conference on Applications of Computer Vision, Wacv 2020 (2020), pp. 1803-1812 [10.1109/WACV45572.2020.9093339] [abs].
  • Kong, F; Chen, C; Huang, B; Collins, LM; Bradbury, K; Malof, JM, Training a single multi-class convolutional segmentation network using multiple datasets with heterogeneous labels: Preliminary results, International Geoscience and Remote Sensing Symposium (Igarss) (2019), pp. 3903-3906 [10.1109/IGARSS.2019.8898617] [abs].
  • Lin, K; Huang, B; Collins, LM; Bradbury, K; Malof, JM, A simple rotational equivariance loss for generic convolutional segmentation networks: Preliminary results, International Geoscience and Remote Sensing Symposium (Igarss) (2019), pp. 3876-3879 [10.1109/IGARSS.2019.8898722] [abs].