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
- Office Location: 140 Science Drive (Gross Hall), Box 90467, Durham, NC 27708
- Office Phone: (919) 613-2411
- Email Address: firstname.lastname@example.org
- Ph.D. Duke University, 2013
Awards, Honors, and Distinctions
- Bass Connections Award for Outstanding Leadership. Duke University. 2022
- ECE 494: Projects in Electrical and Computer Engineering
- EGR 393: Research Projects in Engineering
- 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
- HOUSECS 59: House Course
- IDS 705: Principles of Machine Learning
- 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.
- 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.
- Ren, S., W. Hu, K. Bradbury, D. Harrison-Atlas, L. Malaguzzi Valeri, B. Murray, and J. M. Malof. “Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis.” Applied Energy 326 (November 15, 2022). https://doi.org/10.1016/j.apenergy.2022.119876.
- Calhoun, Z. D., S. Lahrichi, S. Ren, J. M. Malof, and K. Bradbury. “Self-Supervised Encoders Are Better Transfer Learners in Remote Sensing Applications.” Remote Sensing 14, no. 21 (November 1, 2022). https://doi.org/10.3390/rs14215500.
- 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.