Jordan Milton Malof

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

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, GP; Ren, S; Collins, LM; Malof, JM, Mixture Manifold Networks: A Computationally Efficient Baseline for
    Inverse Modeling
    (2022) [abs].
  • Calhoun, ZD; Lahrichi, S; Ren, S; Malof, JM; Bradbury, K, Self-Supervised Encoders Are Better Transfer Learners in Remote Sensing Applications, Remote Sensing, vol 14 no. 21 (2022) [10.3390/rs14215500] [abs].
  • Khatib, O; Ren, S; Malof, J; Padilla, WJ, Learning the Physics of All-Dielectric Metamaterials with Deep Lorentz Neural Networks, Advanced Optical Materials, vol 10 no. 13 (2022) [10.1002/adom.202200097] [abs].
  • 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].