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Aykut ErdemView profile

Associate Professor

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +5 more

Aykut Erdem is an Associate Professor of Computer Engineering at Koç University, affiliated with the KUIS AI Center. He earned his PhD, MSc, and BSc in Computer Engineering from Middle East Technical University (METU), with visiting researcher experiences at Virginia Tech (2004) and MIT (2007). His research focuses on learning-based approaches for visual data understanding, including image editing, visual saliency estimation, and vision-language integration. Research Interests: Vision and Graphics, Machine Learning, Artificial Intelligence, Computer Vision, Natural Language Processing, Generative Artificial Intelligence. Recent work includes text-guided image/video editing, diffusion models for object removal, and GAN-based frameworks for domain adaptation. Scientific Awards: Young Scientist Award (BAGEP 2021) by Science Academy in Computer Engineering Best Paper Award at 5th Multimodal Learning and Applications Workshop (2022) Collaborations and Funding: Principal investigator for TUBITAK 1001 project on generative AI for visual data (2021-2024), Adobe Research Gift (2023), and co-investigator for multiple TUBITAK grants. Serves as Associate Editor for IEEE Transactions on Image Processing (2022-present).

Erkut ErdemView profile

Professor

• Computer Vision
• Machine Learning
• Visual Saliency Prediction
• +5 more

Erkut Erdem is a Professor in the Department of Computer Engineering at Hacettepe University, where he leads the Computer Vision Laboratory (HUCVL). His research focuses on computer vision and machine learning, particularly on incorporating different kinds of context (spatial, temporal and cross-modal) into visual processing across all levels from low to high-level vision. He received his Ph.D. (2008), M.Sc. (2003), and B.Sc. (2001) from Middle East Technical University. Prior to joining Hacettepe University in 2010, he completed a post-doctoral fellowship at Ecole Nationale Supérieure des Télécommunications (2009-2010) and held visiting researcher positions at UCLA (2007) and Virginia Tech (2004). His current research interests include Visual Saliency Prediction, Automatic Image Description, Video/Photoset Summarization, Image Filtering, and Image Editing. Recent work has focused on multimodal learning with video-language models, diffusion-based image editing, and event-based vision for low-light conditions. His research has been published in top venues including NeurIPS, ICLR, ICCV, SIGGRAPH, and ACL. He has received significant recognition including The Young Researcher Award from Turkish Academy of Sciences and being named a 2022 Outstanding Associate Editor of IEEE Transactions on Multimedia. He has secured multiple research projects funded by TUBITAK and received gift funds from Adobe Research for text-guided image synthesis work. Current Teaching: BBM202: Algorithms, AIN434/BBM444: Fundamentals of Computational Photography Graduate Supervision: 6 current Ph.D. students, numerous recent graduates including Burak Ercan (2024) and Aysun Kocak (2023) Professional Affiliations: Co-affiliated with Koç University and İş Bank AI Center (KUIS AI)

• Artificial Intelligence
• Machine Learning
• Data Mining
• +5 more

Prof. Dr. Mehmet Reşit Tolun is a full-time Professor in the Department of Software Engineering at Çankaya University (Turkey) since 2022. Previously held full-time professor positions at Konya Food and Agriculture University (2020-2022), Aksaray University (2013-2017), and TED University (2011-2013), along with a part-time professorship at Başkent University (2017-2020). Specializes in Artificial Intelligence , Machine Learning , and Data Mining , with a focus on deep learning applications in aerospace, biomedical data analysis, and software process improvement. PhD in Computer Science (University of Kent, 1985) MSc in Computer Science (University of Kent, 1982) BSc in Physics and Computer Science (University of Kent, 1981) Research Interests span deep learning frameworks, hybrid expert systems, software engineering methodologies, and biomedical signal processing. Publications emphasize practical implementations in medical diagnostics, robotics, and agricultural pest detection. Scientific Awards include the IEEE Third Millenium Medal (2000). Supervised over 55 graduate students, including Burak Çetin, Uğur Özotuk, and Mahinur Doğan. Collaborated with researchers from Orta Doğu Teknik Üniversitesi , Çankaya University , and Aksaray University .

• Image/Video Processing
• Computer Vision
• Remote Sensing
• +3 more

Prof. Hasan F. Ateş is a Professor at Özyeğin University's Faculty of Engineering, Department of Computer Engineering, specializing in Artificial Intelligence. He joined Özyeğin University as a full-time Professor in 2022 after holding positions at Sabancı University (2004-2005), Işık University (2005-2018), and İstanbul Medipol University (2018-2022). His academic journey began with a B.S. in Electrical and Electronics Engineering from Bilkent University in 1998, followed by M.A. and Ph.D. degrees in Electrical Engineering from Princeton University in 2000 and 2004, respectively. Prof. Ateş's educational background includes: Doctorate in Electrical Engineering, Princeton University, 2004 Master's in Electrical Engineering, Princeton University, 2000 Bachelor's in Electrical and Electronics Engineering, Bilkent University, 1998 His research focuses on the intersection of deep learning and visual information processing, with particular emphasis on image/video processing, computer vision, remote sensing, autonomous systems, and artificial intelligence. Prof. Ateş leads the Deep-VIP Laboratory, which applies deep learning to solve diverse problems in computer vision and image processing. The lab's work spans multiple sectors including defense, telecommunications, medicine, retail, and consumer electronics. Analysis of Prof. Ateş's recent publications reveals a strong focus on advanced deep learning techniques for visual information processing. His work prominently features state-space models, transformer architectures, and hybrid neural approaches applied to problems like super-resolution, 3D reconstruction, object detection, and medical imaging. Notably, there's a growing emphasis on domain adaptation techniques and cross-spectral registration methods, reflecting the increasing complexity of real-world vision applications. Prof. Ateş's contributions have been recognized through several prestigious awards: First place in the 24th Alper Atalay Best Student Paper Competition (2025) Third place in the Master's Thesis category of the 5DT Thesis Competition (2025) As a dedicated mentor, Prof. Ateş has supervised numerous graduate students through their research journeys. His lab, Deep-VIP, has successfully guided students like İ. Can Yağmur (MS thesis on "Self-Supervised Deep Learning for Multispectral Image Matching") and Gökçe Güven (PhD thesis on "DEEP LEARNING TECHNIQUES FOR 3D VOLUME RECONSTRUCTION FROM PLANAR X-RAY IMAGES"). The lab maintains active collaborations with industry partners including Vestel, Tübitak Bilgem, and Obase, securing research funding for cutting-edge projects in computer vision and deep learning. The Deep-VIP Laboratory is at the forefront of deep learning based computer vision research, with current projects focusing on Image Registration, Tiny Object Detection in Aerial Imagery, Building Height Estimation from Satellite Images, 3D Reconstruction, and Super-Resolution Imaging. The lab's recent work has been presented at major conferences including SIU, IGARSS, and ECAI, demonstrating their commitment to advancing both theoretical understanding and practical applications of visual intelligence systems.

• Artificial Intelligence
• Medical Imaging
• Deep Learning
• +3 more

Caner Özer is a Researcher affiliated with Istanbul Technical University's Department of Artificial Intelligence and Data Engineering and the University of Twente's MIA group. He holds a PhD in Computer Engineering from Istanbul Technical University (2020), an MSc in Telecommunication Engineering (2017-2020), and a BSc in Electronics and Communications Engineering (2013-2017). His research focuses on medical imaging AI, explainable artificial intelligence (XAI), deep learning applications in healthcare, and computer vision techniques for artifact detection in medical imaging. He has conducted visiting research at the University of Twente (2024) and serves on academic committees at Istanbul Technical University. Research interests include developing explainable models for mammogram analysis, enhancing medical image quality assessment via transformers and neural networks, and addressing challenges in cardiovascular MRI segmentation through motion artifact detection. His work bridges deep learning theory with practical clinical applications, emphasizing transparency and accuracy in AI-driven medical diagnostics. Notable contributions include cross-domain artifact correction for cardiac MRI, joint CNN-RNN models for intracranial hemorrhage detection, and XAI methods for chest X-ray analysis. His research has been published in top-tier venues with a focus on medical imaging and deep learning advancements.

Tayfun AkgülView profile

Professor

• Image Processing
• Signal Processing
• Audio and Speech Processing
• +5 more

Tayfun Akgül is a Professor at Istanbul Technical University's Faculty of Electrical and Electronics Engineering, Department of Electronics and Communication Engineering. With academic affiliations spanning decades, he combines engineering rigor with innovative research in signal processing and underwater acoustics. His research focuses on advanced signal processing techniques, including Compressive sensing and cyclostationary analysis Underwater acoustic monitoring and sensor arrays Thermal imaging and infrared reflection modeling Biometric identification through facial attributes Seismic signal processing Recent publications demonstrate expertise in Propeller noise analysis in maritime environments Micro-Doppler helicopter signature detection Seismic activity precursor identification Novel fisheye camera human detection systems Awarded Most Successful Doctoral Thesis Award from TESID (2024) IEEE Top 10 Award (2013) he maintains active IEEE membership since 1992 and has led multiple high-impact projects including casualty detection systems and electric vehicle warning systems.

• Signal Processing
• Image Processing
• Machine Learning
• +5 more

Prof. Dr. Bilge Günsel is a distinguished Professor at the Electronics and Communication Engineering Department of Istanbul Technical University . With a career spanning over three decades, she has served in academic and administrative roles including Department Head (2007-2010) and Deputy Head (2005-2006). Her research focuses on Signal Processing , Image Processing , and Machine Learning , with recent work on dynamic convolutional neural networks and video object tracking. Education : PhD (1994) and BS (1985) from Istanbul Technical University in Electronics & Communication Engineering. Research Interests include: Dynamic CNNs for scene classification Video object tracking with deep learning Meta-learning approaches in object verification Transfer learning for crowd density estimation Recent Publications (2021-2024) demonstrate expertise in: Target-driven inference in video tracking Dynamic convolution techniques Instance segmentation integration Scientific Achievements : Scopus h-index of 18 Over 1252 citations Principal Investigator (PI) for two major BAP projects (2016-2019) Collaborations span Turkey and international institutions, with research outputs in partnership with IEEE and academic peers like Furkan Gurkan and Yavuz K. Hanoglu. She continues to lead cutting-edge research while maintaining active teaching and administrative duties.

• Surgery
• Colorectal Surgery
• Proctology
• +3 more

Dr. Ramazan Kozan is a full-time faculty member in the Department of General Surgery at Gazi University Faculty of Medicine since 2019. His primary affiliations include professional memberships in organizations such as the Turkish Surgical Society, Turkish Society of Colon and Rectal Surgery, and the National Endoscopic Laparoscopic Surgery Society. Colorectal Surgery Pelvic Floor Disorders Robotic & Laparoscopic Surgery da Vinci Robotic Console Surgeon His research focuses on surgical interventions for colorectal diseases, pelvic floor disorders, and robotic laparoscopic techniques. He has contributed to the validation of clinical tools and surgical outcomes in colorectal cancer, and his work includes experimental studies on surgical physiology and postoperative complications. The article trends highlight his expertise in colorectal surgery , minimally invasive techniques , and surgical responses to emerging infectious diseases like SARS-CoV-2. His work bridges clinical practice , experimental research , and cross-disciplinary collaboration in surgical innovation. FACS : Fellow of the American College of Surgeons (2023) FEBS-Coloproctology : European Board Qualification in Colorectal Surgery (2024) He has authored over 40 peer-reviewed publications and actively contributes to editorial and review processes in national surgical journals. His certifications include Endoscopy Training from the Turkish Surgical Society and da Vinci Robotic Console Surgeon accreditation.

Selin AslanView profile

Assistant Professor

• Applied Mathematics
• Computational Mathematics
• Imaging Science
• +2 more

Selin Aslan serves as an Assistant Professor in the Department of Mathematics at Koç University, Istanbul, Turkey, where she conducts research at the intersection of computational mathematics and imaging science. Her academic appointments and research activities are centered within the university's mathematics department, contributing to both undergraduate and graduate education in mathematical sciences. Her educational qualifications include: PhD in Mathematics from Virginia Polytechnic Institute and State University (2018) Master's in Mathematics from Rochester Institute of Technology (2013) B.A. in Mathematics from Ege University (2010) Dr. Aslan's research program focuses on developing advanced computational methods for solving inverse problems in imaging, with particular expertise in phase retrieval, tomographic reconstruction, and ptychography. Her work bridges theoretical mathematics with practical applications in medical imaging, microscopy, and materials science, emphasizing algorithmic innovation and computational efficiency. She integrates techniques from deep learning, optimization theory, and high-performance computing to address challenges in image reconstruction under physical constraints. Analysis of her publication record reveals a consistent trajectory toward solving complex imaging problems through hybrid approaches that combine physics-based models with data-driven techniques. Her recent work demonstrates increasing emphasis on scalability for large datasets, robustness in photon-limited scenarios, and real-time processing capabilities, with applications spanning biomedical imaging to advanced microscopy. No scientific awards were documented in the available sources. Information regarding student advising and research grant activities was not specified in the provided materials, though her publication record suggests active research collaboration. Her computational focus implies engagement with high-performance computing resources for large-scale image reconstruction tasks. While specific laboratory infrastructure details were unavailable, her research on multi-GPU implementations and distributed computing indicates utilization of advanced computational facilities for handling large-scale imaging datasets.

• Artificial Intelligence
• Robotics
• Computer Vision
• +4 more

Hulya Yalcin is an Assistant Professor in the Department of Mechanical Engineering at Istanbul Technical University. Her research focuses on artificial intelligence applications in robotics, computer vision, and precision agriculture. She leads projects in musculoskeletal modeling, plant phenology monitoring, and assistive technologies for elderly care. Her work contributes to UN Sustainable Development Goals related to innovation, health, and sustainable agriculture. Key projects include using deep learning for crop yield estimation and developing exergaming systems to improve elderly health. She has authored 52 research outputs and secured funding for initiatives like AISENSE (EU-funded exergames) and plant classification via computer vision. Publications span robotics control, medical engineering, and agricultural informatics. Notable contributions include knee orthosis gait learning via deep reinforcement learning and low-resource Turkish speech recognition improvements. As Principal Investigator, she manages projects on plant classification using CNNs, drone-based depth mapping, and multimodal assisted living systems. Her research bridges AI with practical applications in healthcare, agriculture, and robotics.

Enes AyanView profile

Lecturer

• Artificial Intelligence
• Deep Learning
• Medical Image Analysis
• +3 more

Enes Ayan serves as a Doctor Lecturer in the Department of Computer Engineering at Kirikkale University's Faculty of Engineering and Natural Sciences, where he has maintained continuous academic affiliation since his 2014 appointment as Research Assistant. His career trajectory includes completing both master's (2015) and doctoral degrees (2019) in Computer Engineering at the same institution following his 2013 bachelor's graduation from Süleyman Demirel University. Education Bachelor's: Computer Engineering, Süleyman Demirel University (Isparta), 2013 Master's: Computer Engineering, Kirikkale University, 2015 Doctorate: Computer Engineering, Kirikkale University, 2019 Research Focus Dr. Ayan's work centers on deep learning applications across three primary domains: medical imaging (specializing in dental diagnostics, radiology, and endoscopy), security systems (weapon detection and traffic monitoring), and agricultural technology (crop pest classification). His research bridges theoretical AI advancements with practical healthcare solutions, particularly through explainable AI frameworks for dental caries detection and pneumonia diagnosis. Publication Trends Analysis of his 15 most recent publications (2020-2025) reveals intensifying specialization in dental AI applications (60% of 2024-2025 output), with significant contributions to caries detection under prostheses and tooth numbering systems. Concurrently, his work maintains strong threads in security-focused computer vision (UAV-based traffic analysis, weapon detection) and agricultural AI, demonstrating methodological versatility through genetic algorithm optimizations and ensemble CNN architectures. Professional Context No information regarding student supervision, research grants, laboratory facilities, or scientific awards appears in the source materials. His academic progression from Research Assistant to Doctor Lecturer indicates standard career advancement within Kirikkale University's engineering faculty without notable interruptions.

Mahmut AykaçView profile

Assistant Professor

• Electrical and Electronics Engineering
• Communication Engineering
• Image Processing
• +3 more

Assistant Professor Mahmut Aykaç is affiliated with Gaziantep University, Faculty of Engineering, Department of Electrical and Electronics Engineering. He holds a Doctorate (2010-2018), Master's (2006-2009), and Licence (2001-2006) in Electrical and Electronics Engineering from Gaziantep University, specializing in Circuits and Systems Theory. His research spans Image Processing , Machine Learning , and Wireless Communication Systems , with a focus on RFID localization ZigBee-based navigation Chaos theory in cryptography Edge computing for neural networks Legacy textile system modernization Scientific contributions include 7 refereed journal articles and participation in 7 international conferences. Notable projects: ZigBee Campus Navigation System (2013-2017) Jacquard Loom Control System Modernization (2007-2009) Supervised master's theses on: Single-band GSM RF energy harvesting Automatic power factor correction systems Received certification as expert witness in Expropriation and Electrical Engineering fields.

Mustafa ÖzuysalView profile

Assistant Professor

• Computer Vision
• Object Detection
• Object Tracking
• +5 more

Mustafa Özuysal is an Assistant Professor in the Department of Computer Engineering at the College of Engineering, Izmir Institute of Technology (İYTE), where he leads the Visual Intelligence Research Group. His research focuses on computer vision, including object detection, tracking-by-detection, and real-time scene text recognition, with applications on mobile devices. Research Interests: Large-scale object detection Object detection and tracking on mobile platforms Real-time scene text recognition Feature learning from image and video sequences Augmented reality and camera egomotion estimation His scholarly work includes influential publications in IEEE TPAMI, IET Computer Vision, and top-tier conferences such as CVPR and ECCV, particularly in local feature descriptors like BRIEF and keypoint recognition using random ferns. His research emphasizes efficient, real-time algorithms suitable for embedded and mobile systems. Scientific Contributions: Co-developer of the BRIEF descriptor, widely used in computer vision for fast binary feature matching. Contributor to tracking-by-detection frameworks and homography estimation methods robust to occlusions and viewpoint changes. Dr. Özuysal has taught a range of undergraduate and graduate courses, including Introduction to Image Understanding, Vision-Based Tracking and Modeling, Data Structures, and Mobile Application Development. He advises the Visual Intelligence Research Group and continues to advance research in scalable and efficient vision systems.

Gokberk CinbisView profile

Associate Professor

• data-efficient machine learning
• few-shot learning
• zero-shot learning
• +6 more

Gökberk Cinbiş is an Associate Professor at the Department of Computer Engineering, Middle East Technical University (METU), leading research in data-efficient machine learning with minimal supervision. His work spans zero-shot, few-shot, weakly-supervised, and self-supervised learning, with applications in vision-language integration and large-scale image/video understanding. PhD from Université de Grenoble (2014) M.A. from Boston University (2010) His research focuses on generative models, meta-learning, and robust computer vision systems. Recent work explores sparsity in parameter-efficient fine-tuning, token embeddings for vocabulary extension, and domain-aware LoRA components for personalization. Notable trends in his publications include: Advancements in zero-shot and few-shot learning Applications in remote sensing (SAR2ET for evapotranspiration) Meta-learning and hybrid augmentation techniques Sign language recognition and industrial vision benchmarks Scientific honors include: Science Academy - Young Scientist Award (BAGEP 2024) Google Faculty Research Award (2019-2020) Best PhD Thesis Prize by AFRIF (2015) Alper Atalay Best Student Paper Award (2016) He has supervised MSc students Gencer Sumbul and Berkan Demirel, secured TUBITAK grants on meta-learning and any-shot learning, and organized workshops on industrial inspection at CVPR and ECCV. Cinbiş leads METU ImageLab, focusing on computer vision and machine learning.

Gültekin IşıkView profile

Associate Professor

• Deep Learning
• Machine Learning
• Computer Vision
• +4 more

Assoc. Prof. Gültekin Işık is affiliated with the Department of Computer Hardware at Iğdır University's Faculty of Engineering. He holds a PhD in Computer Engineering from Hacettepe University (2011–2019), focusing on Turkish dialect recognition using deep learning. His academic roles include serving as Head of the Department from 2019 to 2021. His research interests span deep learning applications in computer vision (e.g., YOLO-based crowd detection), optimization algorithms (e.g., Slime Mould Algorithm), environmental modeling (solar power efficiency prediction), and healthcare (breast cancer detection). He has authored numerous peer-reviewed articles and books, including works on convolutional neural networks for plant disease identification and hybrid optimization techniques for data clustering. Prof. Işık teaches advanced courses such as İleri Derin Öğrenme (Advanced Deep Learning) and Sinir Ağları (Neural Networks). His recent work emphasizes real-time video analysis for public health (social distancing) and energy systems optimization. Despite no listed awards, his contributions to interdisciplinary fields like bioacoustics and renewable energy are notable. He has advised two master’s students: Mehmet Şirin Gündüz (2023) on YOLO-based crowd detection and Seda Bayat (2021) on bird species recognition using deep learning. His research integrates theoretical models with practical applications in domains ranging from agriculture to medical imaging.