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 Merged CYGNSS Soil Moisture Product Using a Minimum Variance Estimator (2025)
• Automated Detection of Seafloor Gas Seeps in Multibeam Echosounder Data With an Attention-Guided Convolutional Neural Network (2025)
• Calibrating UAS GNSS-R Measurements Using Water Reflection and Direct Signal As References (2025)
• DREAM-CFA: joint learning of binary color filter array and demosaicing (2025)
• Deep Learning-Based High-Resolution Time-Frequency Domain RFI Suppression in Passive Systems (2025)
• Deep Learning-Based Sequential Processing of Multibeam Echosounder Images for Automated Detection of Seafloor Gas Seep Occurrence (2025)
• Detection and Classification of Gas Seeps in MBES Imagery Using CFAR and Feature-Based Learning (2025)
• Evaluating Depth-Dependent Variability in Machine Learning-Based Seafloor Gas Seep Detection Using Multibeam Echosounder (2025)
• Integrating UAS-Based GNSS-R, LiDAR, and Multispectral Data for Soil Moisture Estimation: Summary of Results From a Three-Year-Long Field Campaign (2025)
• Large Scale Spatial and Temporal Analysis of Vegetation Optical Depth Using Lidar and GNSS-T Fusion (2025)