Jordan Milton Malof

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
  • Faculty Fellow in The Energy Initiative

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

  • Ren, S; Malof, J; Fetter, R; Beach, R; Rineer, J; Bradbury, K, 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, vol 11 no. 4 (2022) [10.3390/ijgi11040222] [abs].
  • Ren, S; Mahendra, A; Khatib, O; Deng, Y; Padilla, WJ; Malof, JM, Inverse deep learning methods and benchmarks for artificial electromagnetic material design., Nanoscale, vol 14 no. 10 (2022), pp. 3958-3969 [10.1039/d1nr08346e] [abs].
  • Deng, Y; Soltani, M; Ren, S; Padilla, W; Khatib, O; Tarokh, V; Dong, J; Malof, J, Data from: Benchmarking deep learning architectures for artificial electromagnetic material problems (2021) [10.7924/r4jm2bv29] [abs].
  • Khatib, O; Ren, S; Malof, J; Padilla, WJ, Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review, Advanced Functional Materials, vol 31 no. 31 (2021) [10.1002/adfm.202101748] [abs].
  • Xu, Y; Huang, B; Luo, X; Bradbury, K; Malof, JM, SIMPL: Generating Synthetic Overhead Imagery to Address Zero-shot and
    Few-Shot Detection Problems
    (2021) [abs].