Jordan Milton Malof

Assistant Research Professor in the Department of Electrical and Computer Engineering

I work with domain experts across different fields to solve challenging real-world problems through the application or development of advanced signal processing, computer vision, machine learning (especially deep learning) methods to real-world problems.  Recently, my work has spanned topics such as remote sensing, energy systems, and materials science.   My work has recently been featured in premiere machine learning conferences (e.g., NeurIps, ICLR) and computer vision conferences (e.g., WACV).

Appointments and Affiliations

  • Assistant Research Professor in the Department of Electrical and Computer Engineering

Contact Information

  • Email Address: jordan.malof@duke.edu

Education

  • Ph.D. Duke University, 2015

Research Interests

Application of advanced machine learning and computer vision techniques to real-world problems

Courses Taught

  • ECE 391: Projects in Electrical and Computer Engineering
  • ECE 392: Projects in Electrical and Computer Engineering
  • ECE 493: Projects in Electrical and Computer Engineering
  • ECE 494: Projects in Electrical and Computer Engineering
  • ECE 590: Advanced Topics in Electrical and Computer Engineering
  • ECE 899: Special Readings in Electrical Engineering
  • EGR 393: Research Projects in Engineering
  • ENERGY 395T: Bass Connections Energy & Environment Research Team
  • ENERGY 795T: Bass Connections Energy & Environment Research Team

Representative Publications

  • Khatib, O., S. Ren, J. Malof, and W. J. Padilla. “Learning the Physics of All-Dielectric Metamaterials with Deep Lorentz Neural Networks.” Advanced Optical Materials 10, no. 13 (July 1, 2022). https://doi.org/10.1002/adom.202200097.
  • 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.
  • Ren, Simiao, Ashwin Mahendra, Omar Khatib, Yang Deng, Willie J. Padilla, and Jordan M. Malof. “Inverse deep learning methods and benchmarks for artificial electromagnetic material design.” Nanoscale 14, no. 10 (March 2022): 3958–69. https://doi.org/10.1039/d1nr08346e.
  • Huang, B., J. Yang, A. Streltsov, K. Bradbury, L. M. Collins, and J. M. 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 15 (January 1, 2022): 4956–70. https://doi.org/10.1109/JSTARS.2021.3124519.
  • 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 15 (January 1, 2022): 4386–96. https://doi.org/10.1109/JSTARS.2022.3172243.