Türker Tekin Ergüzel (born 1978 in Sakarya, Turkey) is a Turkish professor specializing in artificial intelligence and biomedical informatics. Associated with Marmara University and Üsküdar University, he currently serves as the vice rector of Üsküdar University and the director of the Institute of Health Sciences.^[3] His research interests and work include artificial intelligence, neuroimaging, deep learning, and system modeling.^[1,2] He has been honored with the Golden Axon Leadership Award by the Society for Brain Mapping and Therapeutics.

Ergüzel completed his B.Sc., M.Sc., and Ph.D. degrees in computer and control education at Marmara University in 2000, 2003, and 2009, respectively.^[1] His master's thesis focused on the web-based education of data structures, and his doctoral research addressed process control using Genetic-Ant Colony Optimization (ACO) algorithms.^[2] In 2012, he joined the faculty of Üsküdar University, where he has served as a professor and administrator.^[1] In August 2024, he was appointed as the vice rector of Üsküdar University. He also serves as the director of the Institute of Health Sciences at the same institution.

Ergüzel's research focuses on the application of artificial intelligence in healthcare, particularly in neuroimaging-based AI models for diagnostic and prognostic analysis, deep learning algorithms for biomedical data processing, and fuzzy logic and optimization techniques for system modeling. His work has contributed to the development of AI-driven diagnostic tools in neuroscience.

Selected publications by T. T. Ergüzel and colleagues include the 2022 article "Application of deep learning models for neuroimaging in mental disorders," published in the International Journal of Neuroscience (DOI: 10.1080/00207454.2022.2079506). In 2021, they published "Major depressive disorder classification based on different convolutional neural network models: Deep learning approach" in Clinical EEG and Neuroscience (DOI: 10.1177/1550059420914200). Additionally, they authored the 2024 study "The deep learning method differentiates patients with bipolar disorder from controls with high accuracy using EEG data," also published in Clinical EEG and Neuroscience (DOI: 10.1177/15500594211012345).