Ali Gurbuz 

Curriculum Vitae not Provided


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(662) 325-1530

406 Hardy Rd
Ali Cafer Gurbuz (Senior Member, IEEE) received the B.S. degree in electrical engineering from Bilkent University, Ankara, Turkey, in 2003, and the M.S. and Ph.D. degrees in electrical and computer engineering from the Georgia Institute of Technology, Atlanta, GA, USA, in 2005 and 2008, respectively.,From 2003 to 2009, he researched compressive-sensing-based computational imaging problems with Georgia Tech. He held faculty positions with TOBB University and University of Alabama between 2009 and 2017, where he pursued an active research program on the development of sparse signal representations, compressive sensing theory and applications, radar and sensor array signal processing, and machine learning. He is currently an Assistant Professor with the Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA, where he is the Co-Director of the Information Processing and Sensing Laboratory.,Dr. Gurbuz was the recipient of the Best Paper Award for Signal Processing Journal in 2013, the Turkish Academy of Sciences Best Young Scholar Award in Electrical Engineering in 2014, and NSF CAREER Award in 2021. He has served as an Associate Editor for several journals, such as Digital Signal Processing, EURASIP Journal on Advances in Signal Processing, and Physical Communication.
Research Interest
Signal processing & Machine Learning
Deep learning-based Inverse Problems and Signal Processing
Computational imaging, Sparse Signal Processing, Compressive Sensing
Machine Learning for Autonomous Systems, Off-Road Autonomy
UAV based Smart Sensing Systems
Machine Learning for Radar and Remote Sensing Systems
Radar and Array Signal Processing
Selected PublicationsTotal Publications:  15 
Bozdag, E., Nabi, M., Senyurek, V., Kurum, M., & Gurbuz, A. (2023). Fusing Sentinel-1 with CYGNSS to Account For Vegetation Effects in Soil Moisture Retrievals. IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium. Pasadena, CA: IEEE. 2693-2696. DOI:10.1109/IGARSS52108.2023.10281528. [Abstract] [Document Site]

Nabi, M., Senyurek, V., Lei, F., Kurum, M., & Gurbuz, A. (2023). Quasi-Global Assessment of Deep Learning-Based CYGNSS Soil Moisture Retrieval. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. IEEE. 16, 5629-5644. DOI:10.1109/JSTARS.2023.3287591. [Abstract] [Document Site]

Kurum, M., Farhad, M., Senyurek, V., & Gurbuz, A. (2023). Enabling Subfield Scale Soil Moisture Mapping in near Real-time by Recycling L-band GNSS Signals from Drones. EGU General Assembly 2023. EGU23-10991. DOI:10.5194/egusphere-egu23-10991. [Document Site]

Senyurek, V., Farhad, M. M., Gurbuz, A., Kurum, M., & Adeli, A. (2022). Fusion of Reflected GPS Signals With Multispectral Imagery to Estimate Soil Moisture at Subfield Scale From Small UAS Platforms. Journal of Selected Topics in Applied Earth Observations and Remote Sensing. IEEE. 15, 6843-6855. DOI:10.1109/JSTARS.2022.3197794. [Abstract] [Document Site]

Nabi, M., Senyurek, V., Gurbuz, A., & Kurum, M. (2022). Deep Learning-Based Soil Moisture Retrieval in CONUS Using CYGNSS Delay-Doppler Maps. Journal of Selected Topics in Applied Earth Observations and Remote Sensing. IEEE. 15, 6876-6881. DOI:10.1109/JSTARS.2022.3196658. [Abstract] [Document Site]