Jordan Milton Malof

Adjunct Assistant 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

  • Adjunct Assistant 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

Awards, Honors, and Distinctions

  • Bass Connections Award for Outstanding Leadership. Duke University. 2022

Courses Taught

  • ENERGY 795T: Bass Connections Energy & Environment Research Team
  • ENERGY 395T: Bass Connections Energy & Environment Research Team
  • ECE 292: Projects in Electrical and Computer Engineering

Representative Publications

  • Deng, Y., K. Fan, B. Jin, J. Malof, and W. J. Padilla. “Physics-informed learning in artificial electromagnetic materials.” Applied Physics Reviews 12, no. 1 (March 1, 2025). https://doi.org/10.1063/5.0232675.
  • Lu, D., Y. Deng, J. M. Malof, and W. J. Padilla. “Learning Electromagnetic Metamaterial Physics With ChatGPT.” IEEE Access 13 (January 1, 2025): 51513–26. https://doi.org/10.1109/ACCESS.2025.3552418.
  • Yaras, C., K. Kassaw, B. Huang, K. Bradbury, and J. M. Malof. “Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 17 (January 1, 2024): 1988–98. https://doi.org/10.1109/JSTARS.2023.3340412.
  • Spell, Gregory P., Simiao Ren, Leslie M. Collins, and Jordan M. Malof. “Mixture Manifold Networks: A Computationally Efficient Baseline for Inverse Modeling,” November 25, 2022.
  • 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.