معرفی
Assoc. Prof. Dr. Faik Boray Tek is a faculty member in the Department of Artificial Intelligence and Data Engineering at Istanbul Technical University (ITU), Faculty of Computer and Informatics. He joined ITU in August 2022 after serving as an Assistant Professor in Computer Engineering at Işık University from 2009 to 2022. His research bridges artificial intelligence and healthcare, with a strong focus on medical image analysis.
Education:
- PhD in Medical Image Analysis, University of Westminster, UK (2007)
- MSc in Electrical and Electronics Engineering, Middle East Technical University (2003)
- BSc in Electronic Engineering, Başkent University (2000)
His research interests include deep learning, artificial neural networks, medical image analysis, malaria diagnosis, and retinal disease detection. He leverages advanced AI models for diagnosing Alzheimer's disease and diabetic retinopathy using optical coherence tomography (OCT) and OCT angiography (OCTA). His work also extends to Text-to-SQL systems and neuron modeling.
Recent publications (2023–2025) demonstrate a strong trend in medical AI, particularly in ophthalmology and neurodegenerative disease diagnosis. He employs foundational models, segmentation techniques, and hybrid architectures to analyze OCT and OCTA scans. Additional work focuses on time-series forecasting via enhanced recurrent units and systematic reviews of AI in healthcare. His publications appear in IEEE Access, Pattern Recognition, and UBMK proceedings.
Scientific Awards:
- Above Threshold Award, TÜBİTAK (2024)
Dr. Tek leads active research projects funded by TÜBİTAK and ITU-BAP, including "Optical Coherence Tomography (and Angiography) Scans and Deep Learning-Based Alzheimer's Disease Diagnosis" and "Two-Dimensional Adaptive Local Connected Neuron Model". He has advised multiple theses and supervised research outputs, indicating active mentorship. His collaborations include Heidelberg Collaboratory for Image Processing (2015).
He is affiliated with research labs and teams at ITU focused on AI and data engineering, contributing to both academic and applied innovations in medical diagnostics and neural architectures.
