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

  • ECE 292: 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
  • EGR 393: Research Projects in Engineering
  • ENERGY 395T: Bass Connections Energy & Environment Research Team
  • ENERGY 795T: Bass Connections Energy & Environment Research Team

Representative Publications

  • 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.
  • 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.