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

• Office Location: 140 Science Drive (Gross Hall), Box 90467, Durham, NC 27708
• Office Phone: (919) 613-2411
• Email Address: kyle.bradbury@duke.edu
• Websites:
  • LinkedIn
  • Professional Homepage
  • ResearchGate

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].
• Huang, B; Bradbury, K; Collins, LM; Malof, JM, Do Deep Learning Models Generalize to Overhead Imagery from Novel Geographic Domains? the xGD Benchmark Problem, International Geoscience and Remote Sensing Symposium (Igarss) (2020), pp. 1476-1479 [10.1109/IGARSS39084.2020.9323080] [abs].
• Hu, W; Alexander, B; Cathcart, W; Hu, A; Nair, V; Zuo, L; Malof, J; Collins, L; Bradbury, K, Mapping Electric Transmission Line Infrastructure from Aerial Imagery with Deep Learning, International Geoscience and Remote Sensing Symposium (Igarss) (2020), pp. 2229-2232 [10.1109/IGARSS39084.2020.9323851] [abs].
• Nair, V; Rhee, P; Yang, J; Huang, B; Bradbury, K; Malof, JM, Designing Synthetic Overhead Imagery to Match a Target Geographic Region: Preliminary Results Training Deep Learning Models, International Geoscience and Remote Sensing Symposium (Igarss) (2020), pp. 948-951 [10.1109/IGARSS39084.2020.9324180] [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].