جست‌وجوی استاد و پژوهشگر

Engin Erzinمشاهده پروفایل

استاد

• Artificial Intelligence
• Multimedia Systems
• Computer Vision
• +۶ مورد دیگر

Engin Erzin is a Professor at Koç University's College of Engineering, leading the KUIS AI Lab and Multimedia, Vision and Graphics Lab . His research focuses on AI-driven human-centric systems, affective computing, and multimodal interaction analysis. He has contributed extensively to robotics, speech processing, and human-robot interaction through over 70 peer-reviewed publications since 2008. Research interests include: Affective computing and emotion recognition from speech/gestures Human-robot interaction and socially engaging agents Speech-driven animation and gesture synthesis Multimodal data fusion for interaction analysis Deep learning applications in robotics and biomedical engineering Recent work emphasizes: Developing adaptive pHRI controllers for manufacturing tasks Creating engagement measurement frameworks for human-machine interfaces Advancing Turkish speech recognition through self-supervised learning Designing multimodal databases for interaction studies Labs: KUIS AI Lab : Focuses on AI applications in robotics and human-computer interaction Multimedia Lab : Specializes in vision, graphics, and audiovisual analysis

• Human-Computer Interaction
• User Interface Design
• Conscious overload and loss in hypermedia educational environments
• +۷ مورد دیگر

Prof. Dr. Muhammet DEMİRBİLEK serves as a Professor in the Faculty of Education, holding joint appointments across the Department of Educational Sciences, Department of Science, and Department of Curriculum and Instruction. His academic work centers on the intersection of educational technology and human cognition within digital learning environments. His educational background includes a Licence in Electronic Engineering from Istanbul University (1993), a Master's degree in Electrical-Electronic Engineering (with thesis) from Istanbul University (1996), and a Doctor of Philosophy in Education from the University of Florida (2004). DEMİRBİLEK's research examines critical challenges in digital education including conscious cognitive overload in hypermedia environments, user interface effectiveness measurement, and the design of immersive learning systems. His work spans simulation-based environments, 3D virtual spaces, educational gaming mechanics, and social network integration in pedagogy. He investigates how multimedia production impacts teaching efficacy while addressing practical implementation challenges in diverse educational contexts. His publication record (2009-2023) reveals consistent focus on technology-mediated learning across international contexts, particularly examining social media's dual role as both distraction and collaborative tool. Key trends include bibliometric analysis of learning analytics, smartphone usage patterns among students, and cross-cultural studies of ICT adoption in European adult education. His work frequently employs mixed-method approaches to evaluate digital tools like GeoGebra in mathematics instruction and lecture capture systems in specialized fields.

Ismail Sengor Altingovdeمشاهده پروفایل

دانشیار

• Information Retrieval
• Web Search and Mining
• Big Data
• +۳ مورد دیگر

Dr. Ismail Sengor Altingovde serves as an Associate Professor in the Department of Computer Engineering within the College of Engineering at Middle East Technical University (METU) in Ankara, Turkey. Previously, he completed his B.S. and M.S. degrees at Bilkent University, followed by a Ph.D. in Computer Engineering from Bilkent in 2009. His academic journey includes post-doctoral research at Bilkent University (2009-2011) and L3S Research Center in Hannover, Germany (2011-2012) before joining METU. His educational background includes: B.S. in Computer Engineering, Bilkent University (1999) M.S. in Computer Engineering, Bilkent University (2001) Ph.D. in Computer Engineering, Bilkent University (2009) - Thesis: "Improving The Efficiency of Search Engines: Strategies for Focused Crawling, Searching, and Index Pruning" Dr. Altingovde's research focuses on Information Retrieval , particularly Web Search and Mining, Big Data analysis, and Database Management Systems. His work addresses critical challenges in search result diversification, query performance prediction, and scalable indexing techniques. He has pioneered approaches in handling zero-result queries, integrating social signals into search, and developing neural information retrieval models. His research bridges theoretical algorithm design with practical implementations for real-world search engines. His publication record demonstrates a strong trajectory in search technologies, with recent work emphasizing neural information retrieval, social media integration in search, and cross-lingual search systems. The research shows consistent focus on improving search relevance while addressing scalability challenges in modern web-scale systems. His work spans both theoretical contributions and practical implementations with industry relevance. Among his notable recognitions: Distinguished Young Scientist 2016 award (GEBIP) from Turkish Academy of Sciences (TUBA) 2013 Yahoo! Faculty Research and Engagement Program (FREP) award (selected from 27 researchers across 24 countries) Dr. Altingovde actively contributes to the research community through professional service including co-chairing ECIR 2017 short paper track and serving on program committees for CIKM, SIGIR, and Web Science conferences. He has secured multiple research grants from TÜBITAK (Scientific and Technological Research Council of Turkey) including projects on query result caching, semantic relationships for search scalability, and domain-specific search engines. His work connects academic research with practical applications through collaborations with industry partners like Yahoo! Research. He leads research within METU's Computer Engineering Department, contributing to projects like LivingKnowledge (focusing on fact, opinions and bias in time) and previously participating in European projects like MUSCLE (Multimedia Understanding through Semantics, Computation, and Learning). His laboratory work emphasizes experimental validation of search algorithms with real-world datasets and practical implementations.

Reha Civanlarمشاهده پروفایل

استاد

• Computer Networking
• Multimedia Communications
• Wireless Communications
• +۳ مورد دیگر

Reha Civanlar serves as Vice Rector and Founding Dean of the Faculty of Engineering at Ozyegin University, Istanbul, following a distinguished career spanning AT&T Labs-Research, Bell Labs, and DOCOMO USA Labs where he was Executive Vice President (2006-2008). His academic leadership extends to prior roles as Visiting Professor at Koc University (2002-2006) and research department head at major telecommunications laboratories. His educational foundation includes: PhD in Electrical & Electronics Engineering, North Carolina State University (1984) MS in Electrical & Electronics Engineering, METU (1981) BS in Electrical & Electronics Engineering, METU (1976) Dr. Civanlar's research focuses on computer networking and multimedia communications , with pioneering contributions to video compression, wireless systems, and Internet-based multimedia delivery. His work bridges theoretical innovation and practical implementation, particularly in scalable video coding and real-time communication systems. Analysis of his 2015-2021 publications reveals sustained leadership in video conferencing technologies, noise reduction algorithms, and software-defined networking for wireless systems. Key thematic threads include content-adaptive video processing, multi-view video transmission for 3DTV applications, and optimization of multimedia delivery over heterogeneous networks. His scientific recognition includes: IEEE ASSP Group's Best Paper Award (1985) Fulbright Scholar designation IEEE Fellow (2005) Dr. Civanlar has directed major research initiatives including the Visual Communications Research Department at AT&T and DOCOMO's Computer Networks and Media Research Laboratory. He currently provides strategic guidance through advisory roles at Vidyo Inc. (USA) and Argela Teknolojileri AS (Turkey), while shaping academic direction as Vice Rector at Ozyegin University. His laboratory leadership has consistently produced industry-adopted innovations in multimedia communications standards.

• Anesthesiology
• Reanimation
• Pain Management
• +۵ مورد دیگر

Rauf Gül is an Associate Professor at Gaziantep University Faculty of Medicine, Department of Anesthesiology and Reanimation. With a career spanning over two decades, he has established himself as a prominent figure in the field of anesthesiology, particularly in specialized areas such as regional anesthesia, pediatric anesthesia, and airway management. His research interests encompass a wide range of topics within anesthesiology, with particular focus on pain management techniques, regional anesthesia applications, and specialized anesthetic considerations for various surgical procedures. Dr. Gül's work demonstrates particular expertise in managing complex airway cases and developing optimized anesthesia protocols for specific patient populations. Analysis of his recent publications (2023-2025) reveals continued active research in diverse areas including vertebral artery stiffness and tinnitus, TRP channels in ophthalmology, noise effects on anesthetic consumption, fluid management in thoracic surgery, and comparative analgesic effects of different fentanyl administration methods. His work spans both clinical applications and experimental models, demonstrating a comprehensive approach to advancing anesthesiology practice. Dr. Gül has contributed significantly to academic literature with 88 peer-reviewed articles spanning from 2007 to 2025, reflecting sustained research productivity throughout his career. His publications appear in both national and international journals, with many indexed in SCI-Expanded and other major databases. He has also made substantial contributions to educational resources in his field, authoring the book 'Anesthesia for Technicians and Health Schools' (2023) and multiple chapters in 'Complications in Anesthesia' (2010-2012), demonstrating his commitment to training the next generation of anesthesia professionals. His collaborative approach is evident through numerous co-authorships with colleagues across various departments and institutions, particularly with researchers at Gaziantep University and other Turkish medical centers.

• Web Search Engines
• Text-to-SQL Systems
• Online Social Network Analysis
• +۵ مورد دیگر

Özgür Ulusoy is a Professor of Computer Engineering at Bilkent University, Turkey. He has held academic positions since 1993, including Assistant Professor (1993–2003) and Associate Professor (1998–2003) before becoming a full Professor in 2003. He earned his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 1992, following an M.S. (1988) and B.S. (1986) in Computer Engineering from Bilkent University and Middle East Technical University, respectively. His research focuses on web search engines, text-to-SQL systems, social network analysis, multimedia databases, and wireless/mobile systems. Notable projects include developing domain-specific web portals, optimizing query result caching, and exploring semantic relationships for search engine efficiency. He has directed over 20 sponsored projects funded by TÜBITAK, EU, and international collaborations, involving topics like data mining in mobile networks and real-time database scheduling. Ulusoy’s work bridges theoretical contributions with practical implementations, including prototyping video database systems and educational search engines. His research often emphasizes scalability, efficiency, and user-centric design. He has advised graduate students such as Arif Usta and Akifhan Karakayalı. His team’s contributions span algorithm development, system prototyping, and interdisciplinary applications in multimedia, social networks, and cloud computing. Key grants include projects on information retrieval paradigms in SQL translation (TÜBITAK, 2019–2021), refreshment strategies for web search caching (TÜBITAK, 2010–2011), and semantic relationships in search engines (TÜBITAK, 2008–2010). His recent work addresses challenges in IoT sensor networks, cloud resource scheduling, and mitigating misinformation in online platforms.

• Digital Image Processing
• Video Analysis
• Machine Learning
• +۷ مورد دیگر

Erdem Onur Özyurt serves as Assistant Professor in the Department of Mechanical Engineering at Turkish-German University's Faculty of Engineering since 2024, following his tenure as Research Assistant at the same institution since 2016. His academic appointments reflect expertise spanning aerospace systems and sustainable energy engineering. His educational trajectory demonstrates exceptional interdisciplinary breadth: Doctorate in Aerospace and Space Engineering (2018-2024), Istanbul Technical University Master's in Telecommunications Engineering (2013-2018), Istanbul Technical University Bachelor's in Electronics and Communications Engineering (2004-2013), Istanbul Technical University Licence in History (2019-2023), Yeditepe University Associate Degree in Plant and Animal Production (2016-2018), Anadolu University Dr. Özyurt's research integrates machine learning with engineering applications, focusing on digital image processing for spacecraft attitude determination and energy-efficient building technologies. His work in regularization techniques enables robust star identification algorithms while his building energy studies quantify carbon reduction through window film implementations. This dual focus bridges theoretical pattern recognition with practical sustainability solutions. Publication analysis reveals two convergent research streams: spacecraft control systems leverage unsupervised learning for false star filtering and camera motion estimation, while building energy research develops adaptive models for heat gain prediction across diverse geographies. Both domains employ advanced computational methods to solve complex engineering challenges with real-world implementation potential. No scientific awards or major fellowships are documented in available sources. Current information indicates no formal graduate student advisement. No research grants or project funding are specified in the provided materials. Laboratory facilities or dedicated research teams are not referenced in the source documentation.

• Computer Science
• Education
• Software Engineering
• +۱۰ مورد دیگر

Assoc. Prof. Güray Tonguç is an Assistant Professor at Akdeniz University, Faculty of Applied Sciences , Department of Management Information Systems. Previously, he served as Lecturer PhD at Akdeniz University's Department of Informatics (2014-2022) and as a Lecturer at Süleyman Demirel University (2008-2014). Education: Doctorate in Energy, Süleyman Demirel University (2002-2014); Master's in Machine Education, Süleyman Demirel University (2004-2007); Bachelor's in Computer Systems Teaching, Süleyman Demirel University (1997-2002) His research spans Computer Science, Education, and Automation , focusing on computer vision, machine learning, and human-computer interaction. Key areas include fish behavior analysis, educational technology, and driver safety systems . Recent publications show trends in applying image processing to biological studies (2025), machine learning for environmental monitoring (2023), and emotional analytics for educational platforms (2020). He has contributed to holographic education (2020), early warning pictograms (2016), and mobile learning (2016) . With 48 WoS publications and H-indices exceeding 50 in multiple databases, his work bridges interdisciplinary domains like agricultural automation, ergonomics, and sustainable technologies . Currently, he applies AI to distance education dynamics and ecological research.

• Distributed Database Design
• Query Optimization
• Evolutionary Algorithms
• +۸ مورد دیگر

Prof. Ahmet Cosar is a faculty member in the Department of Computer Engineering at the Middle East Technical University (METU), Ankara, Turkey. He holds a PhD in Computer Science from the University of Minnesota (1996), an MS in Computer Engineering from Bilkent University (1988), and a BS in Computer Engineering from METU (1986). His research focuses on distributed database design, query optimization, evolutionary algorithms, computer networks, and cloud computing. He leads the Intelligent Data Analysis Group (IDAG) and teaches courses such as CENG 240, CMPE 275, and CENG 280. Education: PhD, Computer Science, University of Minnesota, 1996 MS, Computer Engineering, Bilkent University, 1988 BS, Computer Engineering, METU, 1986 Research Interests: Query optimization in distributed databases Evolutionary algorithms (e.g., genetic algorithms, particle swarm optimization) Cloud computing and data warehouse design Machine learning applications in cybersecurity Wireless sensor networks and data fusion Publications: Over 50 peer-reviewed articles in journals like Computers & Industrial Engineering and Neurocomputing , focusing on optimization algorithms, cloud resource allocation, and machine learning techniques. Recent work includes island-parallel metaheuristics for graph coloring and reinforcement learning for adaptive interventions. Advising & Grants: Supervised over 30 graduate students (PhD and MS) in areas like evolutionary algorithms, cloud databases, and sensor networks. Active in research grants related to distributed systems and big data. Labs/Teams: Leads the Intelligent Data Analysis Group (IDAG), collaborating on projects in optimization, machine learning, and cloud computing.

• Computer Graphics
• Scientific Visualization
• Crowd Simulation
• +۵ مورد دیگر

Ugur Gudukbay is a Professor in the Department of Computer Science at Bilkent University. He specializes in computer graphics, scientific visualization, and crowd simulation, with extensive contributions to video databases and augmented reality systems. His research explores topics including tetrahedral mesh rendering, human motion animation, and content-based multimedia retrieval. Education Background: Not explicitly stated in the provided text. Research Interests: Real-time rendering techniques Virtual environment modeling Machine learning for video analysis Privacy-preserving data processing Human-computer interaction Advising & Collaboration: Supervised 35+ graduate students since 1998 Co-supervisor in projects involving video databases, medical visualization, and urban crowd simulation Notable collaborations with Dr. Özgür Ulusoy and Dr. Bülent Özgüç Labs/Teams: No specific lab names mentioned, but active in computer graphics and multimedia research groups at Bilkent.

Ediz ŞAYKOLمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Artificial Neural Networks
• +۷ مورد دیگر

Ediz ŞAYKOL is an Assistant Professor and Department Head at Beykent University's Faculty of Engineering and Architecture, Department of Computer Engineering. He also served as Vice Dean from 2012-2015. Prior to his academic career, he worked as a Senior Software Engineer at HAVELSAN A.Ş. from 2004-2010. His educational background includes a Doctorate (2001-2009), Master's degree (1999-2001), and Bachelor's degree (1995-1999), all from İhsan Doğramacı Bilkent University's Faculty of Engineering and Institute of Engineering and Science. His doctoral thesis focused on 'A scenario-based query processing framework for video surveillance'. Professor ŞAYKOL's research spans computer vision, machine learning, and video database systems, with particular emphasis on surveillance applications, medical diagnostics using neural networks, and educational technologies for special needs populations. His work bridges theoretical computer science with practical applications across multiple domains including healthcare, security, education, and entertainment. His publication portfolio shows a consistent trajectory from foundational work in video database management systems to contemporary applications of deep learning across diverse fields. The research demonstrates strong methodological continuity in content-based retrieval techniques while adapting to emerging technologies from early 2000s through present day. 2003 3. Best Paper Award, University, Türkiye Professor ŞAYKOL has supervised over 50 master's and doctoral students, with thesis topics covering computer vision applications, machine learning implementations, and innovative computing solutions for accessibility challenges. His academic leadership extends to curriculum development in computer graphics, operating systems, and computer vision courses taught in both Turkish and English. His laboratory work has focused on video processing systems, with applications in historical document analysis, security surveillance, and medical diagnostics. Current research directions include deep learning applications for behavioral analysis and specialized computing solutions for accessibility challenges.

Adnan YAZICIمشاهده پروفایل

استاد

• Intelligent Database Systems
• Multimedia Databases
• Video Databases
• +۴ مورد دیگر

Prof. Dr. Adnan YAZICI is a full Professor and Director of the Multimedia Database Laboratory at the Department of Computer Engineering, Middle East Technical University (METU), Ankara, Turkey. He earned his Ph.D. in Computer Science from Tulane University, USA, in 1991. Research Areas: Intelligent database systems, multimedia and video databases, information retrieval, wireless multimedia sensor networks, data science, and fuzzy database modeling. Leadership & Editorial Roles: Conference Co-Chair for major IEEE and Very Large Data Bases conferences; Associate Editor of IEEE Transactions on Fuzzy Systems . Professional Affiliations: Member of ACM, IEEE Computational Intelligence Society, and Fuzzy Systems Technical Committee. Awards: IBM Faculty Award (2011) Parlar Foundation’s Young Investigator Award (2001)

• Forensic Sciences
• Forensic Chemistry
• Biochemistry
• +۵ مورد دیگر

Alihan KOCABAŞ is a Research Assistant in the Department of Forensic Sciences at Uskudar University, where he has been serving since 2020. He holds a PhD in Biochemistry from Yıldız Technical University, completed after earning his Master's and Bachelor's degrees in the same institution. His academic profile demonstrates strong interdisciplinary expertise bridging forensic science, biochemistry, and analytical chemistry. BSc in Chemical Engineering, Yıldız Technical University, 2016 MSc in Biochemistry, Yıldız Technical University, 2019 PhD in Biochemistry, Yıldız Technical University KOCABAŞ's research focuses on applying advanced analytical techniques to forensic investigations, with particular emphasis on FTIR spectroscopy applications, biosensor development, and questioned document analysis. His work spans multiple forensic sub-disciplines including biological evidence analysis, trace evidence identification, and crime scene investigation methodologies. He has developed expertise in using spectroscopic methods to analyze various forensic materials including bodily fluids, fabrics, and document evidence. His publication record shows a clear trend toward developing innovative forensic methodologies, particularly using FTIR spectroscopy for identification of condoms, lubricants, urine samples, and contact lenses. His research also explores unconventional forensic applications such as using fruit juices as invisible inks and analyzing the effects of music on youth behavior. The interdisciplinary nature of his work connects biochemistry, materials science, and forensic investigation techniques. KOCABAŞ actively participates in numerous TÜBİTAK-funded research projects, often serving as project head or researcher. His administrative involvement includes multiple committee memberships at Uskudar University including the Education Committee, Faculty Discipline Committee, and Quality Committee. He also serves as advisor for the Dance Club. As an educator, he teaches a diverse range of courses including Forensic Chemistry, Forensic Document Examination, Criminal Behavior, and Biochemistry for Forensic Scientists at both undergraduate and graduate levels. His teaching portfolio reflects his broad expertise across forensic science disciplines.

Gözde YOLCU ÖZTELمشاهده پروفایل

استادیار

• Artificial Intelligence
• Deep Learning
• Computer Vision
• +۵ مورد دیگر

Gözde YOLCU ÖZTEL serves as Assistant Professor in the Department of Software Engineering at Sakarya University's Faculty of Computer and Information Sciences. With a Ph.D. in Computer and Information Engineering completed in 2019, she has established herself as an active researcher in artificial intelligence with significant contributions to computer vision applications. Her educational journey includes: Ph.D. in Computer and Information Engineering (2014-2019) with thesis on deep learning-based interest detection through facial analysis M.Sc. in Computer and Information Engineering (2012-2014) focusing on Kinect-based virtual mirror design B.Sc. in Computer Engineering (2007-2011) from Sakarya University Dr. YOLCU ÖZTEL's research bridges deep learning with practical healthcare and public safety applications. Her work demonstrates exceptional adaptability to emerging challenges, particularly in developing AI systems for pandemic response (masked face detection, social distancing monitoring) and medical diagnostics (monkeypox/skin lesion classification). She specializes in creating efficient mobile-compatible deep learning models that maintain accuracy while operating on resource-constrained devices. Analysis of her 15 most recent publications reveals a clear trajectory toward healthcare-oriented computer vision, with increasing focus on mobile medical applications since 2020. Her research consistently combines novel deep learning architectures with real-world implementation constraints, particularly smartphone deployment. The integration of traditional computer vision techniques (like LBP) with modern deep networks represents a distinctive methodological signature across her work. She actively contributes to research through TÜBİTAK 2209 projects as student advisor and leads institutional research initiatives including SAYZEK-ATP (2024) and MAG Engineering Research Support Group. Her project portfolio spans from mathematical assistant software development to emergency drone-based object detection and MRI-based brain tumor classification. Dr. YOLCU ÖZTEL teaches core software engineering courses including Database Management Systems, Artificial Intelligence Fundamentals, and Data Mining while maintaining active research output. She previously organized the 2017 Facial Expression Recognition workshop, demonstrating ongoing commitment to academic community building.