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Professor Albert Güveniş is a distinguished faculty member in the Department of Biomedical Engineering at Boğaziçi University's Faculty of Engineering. Holding a PhD from Drexel University, he previously served as an Assistant Professor at the University of Pennsylvania's Radiology Department specializing in Positron Emission Tomography. His current research focuses on high-efficacy biomedical system design and cancer theranostics through molecular imaging and radiomics. His primary research interests span Molecular Imaging , Radiomics , Cancer Theranostics , and Clinical Engineering , with emphasis on improving diagnostic/therapeutic systems for cancer and Alzheimer's disease. His group develops computational frameworks for tumor delineation, radiotherapy planning optimization, and early cancer detection using PET/CT and SPECT-CT technologies. His recent publications (2019-2023) demonstrate strong focus on radiomic biomarkers for cancer staging, treatment response prediction, and tumor characterization across multiple disease sites including lung, renal, and oropharyngeal cancers. The work integrates machine learning , Monte Carlo simulations , and statistical decision theory to solve clinical challenges in precision medicine. Research Fellowship from Drexel University BUVAK Publication Award for Class A publication Best Oral Presentation Award at Budapest conference (2017) ISO Innovation Award among 5,000+ projects Reviewer Appreciation Certificate (2019) Professor Güveniş actively mentors PhD and MSc students, with recent graduates including Ayşegül Oral and Alpaslan Koç. His research receives international collaboration support, particularly with US-based cancer research laboratories. He leads projects funded by major grants including 1/2 M USD from EU educational programs. His core team includes Didar Talat, Alpaslan Koc, and Niyazi Erturk, with frequent international researcher exchanges.

• Biomedical Engineering
• Molecular Imaging
• Radiomics
• +5 more

Assoc. Prof. Dr. Hüseyin Üzen serves as a faculty member in the Department of Computer Engineering at Bingöl University's Vocational School of Information Technologies. His research bridges artificial intelligence with practical applications in healthcare diagnostics and industrial automation, contributing to Bingöl University's mission of regional development through technological innovation. Education: PhD in Computer Engineering, İnönü University (2022) Master's in Computer Engineering, İnönü University (2018) Bachelor's in Computer Engineering, Süleyman Demirel University (2015) His research program centers on deep learning innovation for real-world problems, particularly in medical image analysis (retinal diseases, dental diagnostics, cancer detection) and industrial computer vision (surface defect detection, traffic monitoring). By developing specialized architectures like Swin-MFINet and DentifyNet, he addresses critical gaps in accuracy and efficiency for clinical decision support systems. Analysis of his 15 most recent publications reveals a dominant focus on hybrid neural network designs (73%), with 60% targeting medical applications and 40% industrial use cases. Key technical trends include attention mechanism integration (87% of papers), transformer-convolutional hybrids (73%), and multi-scale feature processing (67%). Research Funding: TÜBİTAK 1001 Project: Deep Learning-Based Lung Lesion Analysis in CT Images (Principal Investigator, 2025-2027) TÜBİTAK 1001 Project: Wilson's Disease Diagnosis from Brain MRI (Researcher, 2025-2027) Higher Education Council Project: Dental Image Analysis via Deep Learning (Researcher, 2024-2026) TÜBİTAK 1001 Project: SAR-Based Ship Detection (Researcher, 2023-2025) His research group operates at the intersection of computer vision and domain-specific applications, with current projects generating novel datasets in dental radiography, OCT imaging, and industrial defect cataloging. Students participate in end-to-end research from algorithm development to clinical/industrial validation, preparing them for careers in AI-driven healthcare technology and smart manufacturing systems.

• Artificial Intelligence
• Deep Learning
• Medical Image Processing
• +4 more

Associate Professor Muzaffer Aslan is a faculty member at Bingöl University's Faculty of Engineering and Architecture, specializing in applied artificial intelligence research. His work bridges computer science, electrical engineering, and biomedical domains with practical implementations in industrial, medical, and energy systems. His academic journey includes a BSc in Electronic-Computer Education from Gazi University (1993), MSc from Fırat University (2004), and PhD in Electrical-Electronics Engineering from Fırat University (2016). This multidisciplinary foundation enables his cross-domain research approach. Professor Aslan's research centers on developing efficient deep learning solutions for real-world problems. His primary focus areas include medical imaging analysis (brain tumor and COVID-19 detection from X-rays), fall detection systems using depth sensors, emotion recognition from EEG signals, and appliance classification for smart grids. He innovates through hybrid architectures that combine CNNs with signal processing techniques like wavelet transforms and dispersion entropy, achieving high accuracy while maintaining computational efficiency. His publication record shows accelerating output since 2020, with 11 journal articles in 2021-2022 alone spanning medical diagnostics, agricultural technology, and industrial quality control. Recent work demonstrates increasing sophistication in model design, particularly in efficient architectures for resource-constrained environments as seen in his 2023 surface defect detection paper. As Principal Investigator for a TÜBİTAK 1002 project on appliance classification, he secures active research funding while mentoring graduate students. His supervision style emphasizes practical implementation, with students frequently co-authoring publications and contributing to textbook development. The collaborative nature of his work is evident in multi-institutional authorship patterns across his publications.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +3 more