جست‌وجوی استاد و پژوهشگر

Selma Tekirمشاهده پروفایل

دانشیار

• Natural Language Processing
• Text Mining
• Machine Learning
• +۵ مورد دیگر

Selma Tekir is an Associate Professor in the Computer Engineering Department at Izmir Institute of Technology. She completed her undergraduate studies in Computer Engineering at Ege University in 2001, followed by a master's degree from Izmir Institute of Technology in 2004, and earned her PhD from Ege University in 2010. In 2009, she worked as a visiting researcher at the University of Konstanz, Germany. Her educational background includes: B.Sc.: Computer Engineering, Ege University (2001) M.Sc.: Computer Engineering, Izmir Institute of Technology (2004) Ph.D.: Computer Engineering, Ege University (2010) Dr. Tekir's research spans multiple areas of natural language processing and computational linguistics, with a particular focus on Turkish language processing. Her work explores text mining, news analysis, information warfare, and the application of deep learning techniques to linguistic problems. She has made significant contributions to counterfactual detection in Turkish, multi-modal language models, and the integration of symbolic reasoning with neural approaches. Her research often bridges theoretical advances with practical applications, particularly in the Turkish language context which presents unique challenges as a morphologically rich agglutinative language. Analysis of her recent publications reveals a strong trend toward advancing natural language understanding systems for Turkish, developing methods for counterfactual detection, enhancing question answering systems with knowledge graphs, and applying graph neural networks to biological sequence analysis. Her work demonstrates growing interest in combining symbolic reasoning with neural approaches and exploring self-reflection capabilities in language agents. Dr. Tekir has received research funding for projects including: Consistency and Reliability Assessment in News Chains (TÜBİTAK ARDEB 3501) Application of Data Analysis and Visualization Techniques on Historical Sources (BAP) She teaches courses including CENG 381 - Stochastic Processes, CENG 613 - Scientific Research Methods in Computer Science, and SEDS 501 - Introduction to Data Science.

• RF Power Amplifier Design
• Control Systems
• MEMS-IMU Systems
• +۵ مورد دیگر

Murat Üçüncü is an Associate Professor in the Department of Electrical and Electronics Engineering at Başkent University . He holds a PhD (1989), MSc (1985), and BSc (1983) from Boğaziçi University, and an additional BSc from Kara Harp Okulu (1980). His academic career spans over 35 years, with a focus on RF systems, control theory, and defense technologies. PhD, Boğaziçi University (1989) MSc, Boğaziçi University (1985) BSc, Boğaziçi University (1983) BSc, Kara Harp Okulu (1980) His research interests include RF power amplifier design, MEMS-IMU systems, underwater communication, and graph signal processing for automotive RADAR. Recent work emphasizes adaptive Kalman filtering, avionics network simulations, and autonomous systems for UAVs. Key article trends show expertise in RF circuit optimization , Kalman filter adaptation , and graph-based classification algorithms . No scientific awards are mentioned in the provided data. Advised 21+ students in areas spanning control systems, avionics, and defense technologies Led projects on shaped charges, underwater communication systems, and avionics networks

• Stochastics
• Probability theory
• Stochastic processes
• +۷ مورد دیگر

Lasse Leskelä is an Associate Professor at the Department of Mathematics and Systems Analysis at Aalto University. His research focuses on stochastics, particularly statistical network models, random graphs, and stochastic processes acting on networks. He aims to derive mathematical laws for predicting macroscopic behavior in large random systems and improving statistical learning algorithms for high-dimensional data analysis. Probability theory Stochastic processes Random graphs Statistical network models Queueing Theory Applied probability Leskelä's work spans theoretical and applied domains, with applications in information networks, social networks, biological systems, and financial modeling. His research group actively explores models like random intersection graphs, stochastic block models, and graphons to understand complex systems. Leskelä's recent publications in journals such as the IEEE Transactions on Network Science and Engineering and PLoS Computational Biology highlight his contributions to community detection, hypergraph connectivity, and epidemic modeling. His work bridges mathematical theory with practical applications in data science and network analysis. Varma-Sampo MSc thesis prize , Finnish Mathematical Society McKinsey Prize for the best MSc degree completed in 1999 at the Helsinki University of Technology Leskelä has collaborated with leading researchers across disciplines. His research includes grants from Aalto University and international institutions, focusing on advancing stochastic modeling and data analysis techniques.

• Signal Processing
• Biomedical Signal and Image Processing
• Artificial Intelligence Applications
• +۵ مورد دیگر

Prof. Dr. Aydın Akan is a Professor at the Department of Electrical and Electronics Engineering, Izmir University of Economics. He has held academic and leadership roles at Istanbul University (1996-2017), Izmir Katip Celebi University (2017-2020), serving as Department Chair, Dean of School of Engineering and Architecture, and Director of Biomedical Test and Calibration R&D Center. B.Sc. in Electronics Engineering (1988), Uludag University M.Sc. in Electronics and Telecommunications (1991), Technical University of Istanbul Ph.D. in Electrical Engineering (1996), University of Pittsburgh His research focuses on nonstationary signal processing , time-frequency analysis , and machine learning applications to biomedical engineering and wireless communications. Recent publications emphasize EEG-based diagnostics for neurological disorders, deep learning for biomedical signal classification, and synchronization analysis in brain activity. Article trends highlight interdisciplinary work bridging neuroscience , electrical engineering , and AI , with methodologies like time-frequency transforms, CNNs, and signal decomposition applied to epilepsy, ADHD, and Alzheimer's detection. Scientific honors include: TÜBİTAK scholarship (2004) Senior Member of IEEE Signal Processing and Engineering in Medicine & Biology Societies EURASIP Biomedical Image and Signal Analytics Committee member He has participated in numerous TÜBİTAK and university-funded research projects as principal investigator or researcher, contributed to books/journals, and serves on editorial boards including Elsevier's Digital Signal Processing Journal.

• Control Theory and Applications
• Artificial Intelligence
• Machine Learning

Mert Can is a Research Assistant at Istanbul Technical University in the Control and Automation Engineering department. His work spans interdisciplinary fields combining control theory with artificial intelligence. Doctorate in Control and Automation Engineering (2020) Master's in Control and Automation Engineering (2017) Bachelor's in Control and Automation Engineering (2012) His research focuses on: Optimizing variable-order fractional derivatives for adaptive control systems Applying AI to friction damper distribution in seismic protection Developing robust loss functions using fractional calculus Recent publications demonstrate expertise in quadcopter position control and graph neural networks for neuroimaging data fusion. Key trends include: Integration of AI with traditional control systems Advancements in fractional calculus applications Optimization methods for structural engineering No scientific awards are currently recorded.

• Signal Processing
• Image Analysis
• Medical Diagnostics
• +۵ مورد دیگر

Burak Acar is a Full-Time Professor of Electrical and Electronics Engineering at Bogazici University, Turkey, and director of the VAV Lab. He holds affiliations with the Bogazici University Center for Life Sciences and Koç University’s KUIS AI Lab. His academic journey includes a BS, MS, and PhD in Electrical and Electronics Engineering from Bilkent University (1994–2000), followed by postdoctoral research at Stanford University’s Medical School (2000–2003). He has served as a Mercator Visiting Professor at TU Munich (2012–2013). His research focuses on signal/image processing for medical and industrial diagnostics, network modeling, and machine learning applications. Key interests include Alzheimer’s disease diagnostics, medical imaging analysis, and predictive maintenance systems. He leads interdisciplinary projects in computational neuroscience and AI-driven medical diagnostics. Education: PhD, Electrical and Electronics Engineering, Bilkent University (2000) MS, Electrical and Electronics Engineering, Bilkent University (1996) BS, Electrical and Electronics Engineering, Bilkent University (1994) Awards: Excellence in Research Award, Bogazici University Foundation (2006) Young Researcher Award, Turkish Academy of Sciences (2008) His work spans over 2000 citations and includes 7 US patents. Current research emphasizes brain connectivity analysis for Alzheimer’s disease, AI-driven medical imaging tools, and industrial diagnostics through machine learning. He collaborates internationally and directs the VAV Lab, which develops cutting-edge algorithms for medical and engineering challenges.

Ayşe Tosun Kühnمشاهده پروفایل

دانشیار

• Software Engineering
• Software Quality
• Defect Prediction
• +۹ مورد دیگر

Ayşe Tosun Kühn is an Associate Professor in the Department of Computer Engineering at Istanbul Technical University (ITU), where she also serves in administrative roles such as Deputy Dean and Management Board Member of the ITU Artificial Intelligence and Data Science Application and Research Center. She earned her Ph.D. in Computer Engineering from Boğaziçi University and has held academic positions at Sabancı University and the University of Oulu. Her research lies at the intersection of software engineering, empirical methods, and data science, with a focus on software quality, defect prediction, technical debt, vulnerability analysis, and test-driven development. She leads numerous funded research projects, including those supported by TÜBİTAK and industry collaborations, applying machine learning and reinforcement learning to software engineering challenges. Her recent publications reflect a strong trend in applying advanced machine learning techniques—particularly deep learning and reinforcement learning—to problems in software analytics, developer behavior modeling, and delivery optimization. She has made significant contributions to empirical studies on test-driven development, defect prediction models, and vulnerability detection in code. Her work is frequently published in high-impact journals such as IEEE Transactions on Software Engineering, Empirical Software Engineering, and Journal of Systems and Software. She has served as a guest editor and editorial board member for the Turkish Journal of Electrical Engineering and Computer Sciences , and has contributed book chapters on data science in software engineering. Her research has been cited over 1,700 times, with an h-index of 23. Scientific Awards: None explicitly mentioned. She advises several graduate students and has collaborated extensively with researchers in Turkey and internationally. She has also contributed to industry applications through projects on software measurement, defect prediction, and AI-based analytics. She is actively involved in organizing and contributing to major software engineering conferences and workshops. She is a key member of ITU’s AI and Data Science initiatives, contributing to both academic research and practical implementations in software engineering.

Numan CELEBİمشاهده پروفایل

استاد

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

Prof. Dr. Numan CELEBİ serves as a Professor at Sakarya University's Faculty of Computer and Information Sciences, Department of Information Systems Engineering, where he has held academic positions since 2007. His career progression includes promotion to Associate Professor in 2013 and subsequent advancement to full Professor. His educational foundation comprises a Doctorate in Industrial Engineering from Sakarya University (1998-2004) with thesis Inductive-rough clustering approach to part family generation , a Master's in Electrical and Electronics Engineering (1995-1997) with thesis Development of computer program for the implementation of Adapazari medium voltage distribution network (SCADA) system , and a Licence from Istanbul Technical University's Electrical-Electronic Engineering program (1985-1989). CELEBİ's research spans Artificial Intelligence , Machine Learning , and Computer Vision , with significant contributions to optimization algorithms (Polar Bear Algorithm, Tug of War Optimization), intelligent transportation systems (traffic congestion detection, vehicle rerouting), and computer vision applications (object tracking, saliency detection, UAV-based plant recognition). His methodology frequently integrates Rough Set Theory and fuzzy systems for data analysis and decision support. Analysis of his 15 most recent publications (2007-2023) reveals a clear research trajectory toward applying metaheuristic optimization and deep learning to real-world problems. His work demonstrates increasing focus on transportation systems (40% of recent publications), agricultural technology via UAVs (15%), and novel optimization frameworks (25%), with consistent methodological emphasis on hybrid algorithm design and real-time implementation. As an educator, CELEBİ supervises graduate research through courses like ENF 524 Project and teaches specialized subjects including Meta Heuristic Optimization Methods , Intelligent Techniques in Data Analysis , and Data Science across undergraduate and graduate programs. His teaching portfolio spans discrete mathematics, computer networks, and cloud computing, reflecting interdisciplinary expertise.

Emine Sümeyra Turalı Emreمشاهده پروفایل

استادیار

• Chiral nanoparticles
• Antibacterial nanoparticles
• Extracellular vesicle capturing
• +۵ مورد دیگر

Dr. Emine Sümeyra Turalı-Emre is an Assistant Professor at the Institute of Biomedical Engineering, Bogazici University, where she joined in 2024. She combines expertise in nanotechnology, biomedical engineering, and data science to solve critical healthcare challenges. Previously, she was a Postdoctoral Research Fellow in Chemical Engineering at the University of Michigan (2021-2024) and completed her PhD in Biomedical Engineering from the same institution in 2021. Her educational background includes: PhD in Biomedical Engineering, University of Michigan, 2021 MSc in Biomedical Engineering, University of Michigan, 2015 BSc in Molecular Biology and Genetics, Istanbul University, 2008 Dr. Turalı-Emre's research focuses on engineering inorganic nanoparticles for applications such as drug and gene delivery, antibacterial and anticancer treatments, bone regeneration, and extracellular vesicle capturing for diagnostics. Her work spans chiral nanoparticles, antibacterial nanoparticles, extracellular vesicle capturing, AI-driven nanoparticle-protein interactions, drug and gene delivery systems, cancer therapies, and translational nanotechnology. She approaches biomedical challenges through multidisciplinary collaborations that bridge engineering, life sciences, and computer science. Her recent publications in leading journals like Advanced Materials, Matter, and PNAS demonstrate strong trends in chiral nanomaterials for diagnostics and therapeutics, antibacterial applications against biofilms and antibiotic-resistant bacteria, and the integration of artificial intelligence with nanotechnology. Her work shows a clear progression from fundamental nanoparticle synthesis to translational applications in healthcare. Scientific awards and recognitions include: BioInterfaces Research Community Innovator Award, 2024 Full Member, Sigma Xi, The Scientific Research Honor Society, 2024 Women in Science and Engineering, Cinda Sue Davis STEM Equity Leadership Award (Nominee), 2024 Women in Science and Engineering, Willie Hobbs Moore Achievement Award (Nominee), 2024 Selected Participant, AI in Science & Engineering Summer Academy, 2023 And several other prestigious awards and recognitions Dr. Turalı-Emre is actively involved in mentoring students and fostering interdisciplinary collaboration. She currently leads a TÜBİTAK-BİLGEM project developing an AI-driven database to optimize antibacterial nanoparticles for diagnostics and treatments, in collaboration with Dr. Betül Özateş from the Institute of Data Science and Artificial Intelligence. She has also contributed to Coulter Translational Research Partnership projects focused on extracellular vesicle capture, optimizing RNA isolation from biofilms, and developing antibacterial surfaces using chiral nanoparticles. Her laboratory provides a vibrant environment for undergraduate, graduate, and non-traditional students from various fields, including engineering, life sciences, and computer science, to conduct impactful research and contribute to transformative innovations in healthcare.

Dilek Günneç Danışمشاهده پروفایل

دانشیار

• Combinatorial optimization
• Network optimization models
• Social network analysis
• +۷ مورد دیگر

Dilek Günneç Danış is an Associate Professor in the Industrial Engineering Department at Özyeğin University, Turkey. Her academic work spans network optimization, social network analysis, and humanitarian logistics with applications in disaster management, marketing, and public health. Ph.D. in Operations Management/Management Science, University of Maryland (2012) M.Sc. in Industrial Engineering, Koç University (2007) B.Sc. in Mathematics with Minor in Operations Research, Middle East Technical University (2005) Research Interests : Combinatorial optimization and complex systems Network modeling for information propagation New product development under social influences Humanitarian logistics in refugee crises Disaster mitigation and infrastructure resilience Scientific Contributions : Developed agent-based models for refugee migration paths Created capacitated mobile facility location frameworks Advanced influence maximization algorithms for social networks Optimized disaster response logistics systems Scientific Awards : Best Poster Award (Business Analytics Workshop, 2017) Advised team: Third Place INFORMS Student Competition (2020)

Can Özturanمشاهده پروفایل

استاد

• Blockchain Technologies
• Parallel Processing
• High Performance Computing
• +۴ مورد دیگر

Can Özturan is a Professor in the Department of Computer Engineering at Bogazici University in Istanbul, Turkey. He joined the department as a faculty member in 1996 after completing his Ph.D. in Computer Science from Rensselaer Polytechnic Institute in 1995 and working as a Postdoctoral Staff Scientist at the ICASE (Institute for Computer Applications in Science), NASA Langley Research Center. His research interests span Blockchain Technologies, Parallel Processing, High Performance Computing, Graph Algorithms, Scientific Computing, Resource Management, and Grid/Cloud Computing. Over his career, his research focus has evolved from parallel processing and graph algorithms toward blockchain technologies, particularly focusing on privacy-preserving techniques, fraud detection, and performance optimization in Ethereum systems. An analysis of his recent publications (2021-2025) reveals a strong concentration on blockchain applications, with particular emphasis on privacy-preserving protocols using zero-knowledge proofs, fraud detection in transaction graphs, and performance optimization for blockchain systems. His work bridges theoretical computer science with practical blockchain implementations, often applying parallel processing techniques to blockchain challenges. Professor Özturan teaches courses in Blockchain Programming (CMPE483), Parallel Processing (CMPE478), Systems Programming (CMPE230), Graph Algorithms (CMPE528), and Compiler Design (CMPE425), demonstrating his expertise across both foundational and emerging areas of computer engineering.

Tolga Ayavمشاهده پروفایل

استاد

• Formal Methods for Software Testing
• Hardware Component and On-Chip System Tests
• Real-Time and Fault-Tolerant Embedded Systems
• +۵ مورد دیگر

Professor Tolga Ayav is affiliated with the Department of Computer Engineering at Izmir Institute of Technology , where he has served as faculty since 2006. He received his BSc in Electrical and Electronics Engineering (1995) from Dokuz Eylül University, MSc in Computer Engineering (1999) from Izmir Institute of Technology, and PhD in Computer Engineering (2004) from Ege University. He was a researcher at INRIA Rhone-Alpes in 2005. Education BSc: Electrical and Electronics Engineering, Dokuz Eylül University (1995) MSc: Computer Engineering, Izmir Institute of Technology (1999) PhD: Computer Engineering, Ege University (2004) Research Interests include formal methods for software testing, hardware component and on-chip system tests, real-time and fault-tolerant embedded systems, blockchain applications, and machine learning. His work focuses on: Formal verification techniques Real-time system optimization Hardware-software co-design Blockchain-based security solutions Mathematical modeling in testing Recent Publications span topics like: Deep learning for livestock monitoring Blockchain in IoT security Fourier expansion in fault analysis Optimized data replication strategies Finite state machine testing Scientific Awards include: Best Paper Award at IEEE CBDCom 2016 for cloud data replication research Teaching includes courses such as: CENG 312: Computer Networks CENG 523: Advanced Topics in Real-Time Systems CENG 215: Circuits and Electronics Projects he has led include: TÜBİTAK TEYDEB: Spectrally Efficient Small Cell Base Station Design BAP: Dedicated Server for Physical Network Applications Lab Leadership : He leads the DCS Research Group at Izmir Institute of Technology, focusing on distributed computing and real-time systems.

Belgin Ergenç Bostanoğluمشاهده پروفایل

دانشیار

• Database Systems
• Data Mining
• Privacy-Preserving Techniques
• +۳ مورد دیگر

Belgin Ergenç Bostanoğlu is an Associate Professor in the Computer Engineering Department at Izmir Institute of Technology (Turkey). Her research focuses on query optimization in distributed databases, association rule mining, privacy-preserving data mining, and graph-based algorithms. She leads the Dworld research laboratory and has held academic and industry roles since the 1980s. Education: B.Sc. in Computer Engineering, Middle East Technical University (1983) M.Sc. in Computer Engineering, Izmir Institute of Technology (2002) Ph.D. in Computer Engineering, Paul Sabatier University, France (2008) Research Interests: Dynamic frequent itemset mining and hiding under multiple support thresholds Subgraph mining in evolving graphs Federated query processing over linked data Privacy-preserving techniques in distributed databases Medical NLP applications (e.g., TurkMedNLI dataset) Her recent work emphasizes large-scale graph analysis, medical NLP dataset development, and adaptive join operators for federated SPARQL queries. She has contributed to over 30 peer-reviewed publications and led projects like the TÜBİTAK ARDEB 3501 platform for dynamic frequent itemset mining. Teaching: Courses include Advanced Database Management Systems, Knowledge Discovery, and Privacy-Preserving Data Mining. Labs & Projects: Manages Dworld lab and coordinates projects such as 'Turkish Medical NLP Model Development' (BAP-funded) and the Behavioral Next Generation Wireless Networks COST Action.

Necati ARASمشاهده پروفایل

استاد

• Inventory Control
• Product Recovery Network Design
• Reverse Logistics Management
• +۷ مورد دیگر

Necati ARAS is a Professor in the Department of Industrial Engineering at Bogazici University. He holds a PhD from Bogazici University (1999). His research focuses on inventory control, reverse logistics, supply chain optimization, and the application of artificial neural networks in complex systems. He has published extensively on topics including facility location, wireless sensor networks, disaster preparedness, and competitive market strategies. Education: PhD, Bogazici University, 1999 ARAS's work bridges theoretical optimization with real-world applications, particularly in logistics, network security, and smart cities. His contributions include models for minimizing contagion spread in networks, optimizing multi-tier cloud computing, and improving vehicle routing efficiency. Recent research emphasizes strategic resource allocation and mitigating misinformation in social networks. Despite no listed awards, his prolific publication record (over 70 articles) reflects sustained academic impact. His research often addresses humanitarian challenges like disaster response and infrastructure protection. ARAS has advised numerous projects but no specific student names are provided in the text. His research teams likely focus on interdisciplinary collaboration between operations research, computer science, and civil engineering.

Shahram TAHERIمشاهده پروفایل

استادیار

• Biomedical Image
• Signal Processing
• Deep Learning
• +۲ مورد دیگر

Shahram TAHERI is an Assistant Professor in the Department of Computer Engineering at Antalya Bilim University (ABU), Turkey, since 2019. Previously, he served as a faculty member at Azad University, Iran, for 8 years. He holds a Ph.D. in Computer Engineering from Eastern Mediterranean University, a Master’s from Shiraz University, and a Bachelor’s from Shahid Bahonar University of Kerman. Research Interests: Biomedical Image Processing Signal Processing Deep Learning Pattern Recognition Neural Networks Key Trends in Publications: His work focuses on applying deep learning and neural networks to biomedical image and signal processing, particularly in age estimation, cancer histopathology, and animal classification. His research spans both multi-stage feature fusion and magnification-specific analysis.