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Email
gurbuz@ece.msstate.edu

Office
Simrall 325

Phone
(662) 325-1530

Address
406 Hardy Rd
MS
Gurbuz
Ali
Gurbuz
Center for Advanced Vehicular Systems
Faculty


Email
gurbuz@ece.msstate.edu

Office
Simrall 325

Phone
(662) 325-1530

Address
406 Hardy Rd
MS
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 Publications Total Publications:  19 
Nabi, M., Senyurek, V., Kurum, M., & Gurbuz, A. (2024). Best Linear Unbiased Estimators for Fusion of Multiple CYGNSS Soil Moisture Products. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. IEEE. 1, 1-11. DOI:10.1109/JSTARS.2024.3443100. [Document Site]

Rafi, M., Senyurek, V., & Gurbuz, A. (2024). Performance Assessment of Crop Line Detection in Corn Field from Unmanned Aerial Vehicle Video. SPIE 13053, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IX. National Harbor, Maryland, United States: SPIE. 13053, 89-98. DOI:doi.org/10.1117/12.3013501. [Abstract] [Document Site]

Hodges, E., Chew, C., Al-Khalidi, M., Ouellette, J., Johnson, T., Lei, F., Kurum, M., Gurbuz, A., & Senyurek, V. (2024). A Blended CYGNSS Soil Moisture Product Partitioned with Ancillary Data. 2024 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM). Boulder, CO, USA: IEEE. 174. DOI:10.23919/USNC-URSINRSM60317.2024.10464722. [Abstract] [Document Site]

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]

Hicks, B., Ayna, C. O., Senyurek, V., Gupta, S., Skarke, A., & Gurbuz, A. (2023). Machine Learning Based Automated Detection of Seafloor Gas Seeps. OCEANS 2023 - MTS/IEEE U.S. Gulf Coast. Biloxi, MS, USA: IEEE. 1-6. DOI:10.23919/OCEANS52994.2023.10337038. [Abstract] [Document Site]