Directory at the HPC²
Biography
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
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 Publications
Total Publications: 12
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]
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]
Lei, F., Senyurek, V., Kurum, M., Gurbuz, A., Boyd, D., Moorhead, R. J., & Crow, W. T. (2022). Quasi-global Machine Learning-based Soil Moisture Estimates at High Spatio-temporal Scales Using CYGNSS and SMAP Observations. Remote Sensing of Environment. Elsevier. 276, 113041. DOI:10.1016/j.rse.2022.113041. [Abstract] [Document Site]
Senyurek, V., Lei, F., Gurbuz, A., Kurum, M., & Moorhead, R. J. (2022). Machine Learning-based Global Soil Moisture Estimation Using GNSS-R. SoutheastCon 2022. Mobile, AL, USA: IEEE. 434-435. DOI:10.1109/SoutheastCon48659.2022.9764039. [Abstract] [Document Site]
Senyurek, V., Farhad, M., Gurbuz, A., Kurum, M., & Moorhead, R. J. (2022). SoilMoistureMapper: a GNSS-R Approach for Soil Moisture Retrieval on UAV. UAAAI-22 AI for Agriculture and Food Systems (AIAFS) Workshop. Vancouver, BC (Canada). [Abstract] [Document Site]
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]
Lei, F., Senyurek, V., Kurum, M., Gurbuz, A., Boyd, D., Moorhead, R. J., & Crow, W. T. (2022). Quasi-global Machine Learning-based Soil Moisture Estimates at High Spatio-temporal Scales Using CYGNSS and SMAP Observations. Remote Sensing of Environment. Elsevier. 276, 113041. DOI:10.1016/j.rse.2022.113041. [Abstract] [Document Site]
Senyurek, V., Lei, F., Gurbuz, A., Kurum, M., & Moorhead, R. J. (2022). Machine Learning-based Global Soil Moisture Estimation Using GNSS-R. SoutheastCon 2022. Mobile, AL, USA: IEEE. 434-435. DOI:10.1109/SoutheastCon48659.2022.9764039. [Abstract] [Document Site]
Senyurek, V., Farhad, M., Gurbuz, A., Kurum, M., & Moorhead, R. J. (2022). SoilMoistureMapper: a GNSS-R Approach for Soil Moisture Retrieval on UAV. UAAAI-22 AI for Agriculture and Food Systems (AIAFS) Workshop. Vancouver, BC (Canada). [Abstract] [Document Site]
