Ali Gurbuz

Biography

Ali Gurbuz received his B.S. in electrical engineering from Bilkent University, Ankara, Turkey, and his M.S. and Ph.D. degrees in electrical and computer engineering from the Georgia Institute of Technology in Atlanta. Prior to joining the North Carolina State University faculty, he was a postdoctoral fellow at Georgia Tech and held assistant and associate professor positions at Mississippi State University. He is a National Science Foundation Faculty Early Career Development (CAREER) awardee and a Turkish Academy of Sciences Best Young Scholar. He is currently an associate editor for Institute of Electrical and Electronics Engineers Transactions on Aerospace and Electronic Systems.

Gurbuz’s research integrates signal/image processing and machine learning for radar, remote sensing and wireless communication applications. His interests include compressive sensing, physics aware and explainable machine learning for inverse problems, computational imaging, multi sensor activity sensing, radar/communication coexistence, passive sensing, unmanned aerial vehicle (UAV)-based remote sensing and precision agriculture.

Education

• Ph.D. 2008
  Electrical and Computer Engineering
  Georgia Institute of Technology
• Master's 2005
  Electrical and Computer Engineering
  Georgia Institute of Technology
• Bachelor's 2003
  Electrical and Electronics Engineering
  Bilkent University

Recent Publications

• A Physical Testbed and Open Dataset for Passive Sensing and Wireless Communication Spectrum Coexistence (2024)
• Beam Coefficient Prediction for Antenna Arrays Using Physics-Aware Convolutional Neural Networks (2024)
• Best Linear Unbiased Estimators for Fusion of Multiple CYGNSS Soil Moisture Products (2024)
• Emerging Trends in Autonomous Vehicle Perception: Multimodal Fusion for 3D Object Detection (2024)
• HRSpecNET: A Deep Learning-Based High-Resolution Radar Micro-Doppler Signature Reconstruction for Improved HAR Classification (2024)
• PLFNets: Interpretable Complex-Valued Parameterized Learnable Filters for Computationally Efficient RF Classification (2024)
• SDR-Based Dual Polarized L-Band Microwave Radiometer Operating From Small UAS Platforms (2024)
• A Ubiquitous GNSS-R Methodology to Estimate Surface Reflectivity Using Spinning Smartphone Onboard a Small UAS (2023)
• CV-SincNet: Learning Complex Sinc Filters From Raw Radar Data for Computationally Efficient Human Motion Recognition (2023)
• Learning-Based Optimization of Hyperspectral Band Selection for Classification (2023)