Jordan Malof

Jordan Malof is an Adjunct Assistant Professor in the Department of Electrical and Computer Engineering at Duke University. He conducts interdisciplinary research applying advanced signal processing, computer vision, and machine learning (particularly deep learning) techniques to real-world problems in remote sensing, energy systems, and materials science.

His notable awards include the 2022 Bass Connections Award for Outstanding Leadership at Duke University. He has taught courses such as ENERGY 795T: Bass Connections Energy & Environment Research Team and ECE 292: Projects in Electrical and Computer Engineering.

Malof's research spans several key areas:

• Remote Sensing Applications
• Deep Learning for Electromagnetic Materials
• Energy Infrastructure Mapping
• Computer Vision in Geospatial Analysis
• Material Science Modeling
• AI-Driven Solar Energy Assessment

His recent publications in premier venues like NeurIPS and WACV demonstrate expertise in:

• Physics-informed neural networks
• Metamaterial design optimization
• Domain adaptation techniques
• Energy security assessment frameworks
• Computational electromagnetics
• Geospatial data analysis

Scientific awards:

• 2022 Bass Connections Award for Outstanding Leadership

Malof's collaborative approach involves working with domain experts across disciplines to develop novel AI methodologies for complex engineering challenges, particularly in energy systems and electromagnetic material design.