جست‌وجوی استاد و پژوهشگر

• Real-Time Systems
• Multiprocessor Scheduling
• Real-Time Operating Systems
• +۴ مورد دیگر

Cláudio Maia is a Lecturer and Integrated PhD Researcher at the Faculty of Engineering of the University of Porto, Portugal. He holds a B.Sc. and M.Sc. in Computer Engineering from the Polytechnic Institute of Porto and a Ph.D. in Electrical and Computer Engineering from the University of Porto (2018). His research focuses on real-time systems, including real-time operating systems, hypervisors, multiprocessor architectures, and scheduling theory. He has contributed to advancements in task scheduling for multiprocessor platforms, resource contention analysis, and WCRT (Worst-Case Response Time) methodologies. His work has been published in venues such as RTCSA, RTNS, and DATE, and includes a Best Paper award at ICESS 2021. He is affiliated with the CISTER Research Centre, where he collaborates on projects involving embedded systems, real-time critical CPS, and automotive systems integration. His recent research explores memory and bus contention analysis in real-time task models, with applications to multiprocessor systems and industrial automation. Education: Bachelor’s Degree (B.Sc.) in Computer Engineering, School of Engineering, Polytechnic Institute of Porto Master’s Degree (M.Sc.) in Computer Engineering, School of Engineering, Polytechnic Institute of Porto Ph.D. in Electrical and Computer Engineering, University of Porto (2018) Research Interests: Real-Time Scheduling (WCRT Analysis, Partitioned/Global Scheduling) Multiprocessor Architectures and Resource Contention Real-Time Operating Systems and Hypervisors Parallel Task Models (Fork-Join, 3-Phase Tasks) Embedded Systems and Cyber-Physical Systems (CPS) Awards: Best Paper Award at ICESS 2021 for 'Bus-Contention Aware WCRT Analysis' Key Contributions: Developed contention-aware scheduling models for real-time multiprocessor systems Advanced WCRT analysis techniques for 3-Phase Task Models Contributed to frameworks for automotive systems integration and CPS emulation Labs/Teams: Active member of the CISTER Research Centre, collaborating on industrial and academic projects in real-time systems and embedded computing.

Muhammet Mustafa Kahramanمشاهده پروفایل

دانشیار

• Mining Methods and Mine System Analysis
• Mining Economics
• Mine Design and Operation
• +۷ مورد دیگر

Assoc. Prof. Dr. Muhammet Mustafa Kahraman is an Associate Professor at the Department of Mining Engineering, Faculty of Mines, Istanbul Technical University. His research focuses on Mining Methods, Mine System Analysis, Mining Economics, Mine Design, and Occupational Safety in Mines. He holds a PhD from the University of Arizona and has extensive industry experience in data-driven solutions for mining operations. Education: PhD in Mining and Geological Engineering from University of Arizona (2011–2015), BSc in Mining Engineering from ITU (2002–2007) Research interests include integrating computer vision, automation, and data analytics into mining operations. His work addresses challenges in mine safety, environmental impact, and operational efficiency. Recent projects involve real-time fleet management systems and dust control in coal mines. He actively collaborates with global mining firms like Rio Tinto and Peabody Energy. Notable publications cover topics such as lean mine management, blockchain for conflict minerals, and predictive maintenance using sequential pattern mining. He has supervised multiple theses on occupational safety legislation and mine planning. Academic roles include Vice President of the Mining Engineering Department at ITU and former Head of Department at Gumushane University. He has coordinated international projects on mine automation and data-driven decision-making.

• Production Planning
• Geological Modeling
• Resource Estimation
• +۷ مورد دیگر

Dr. Kürşat Hasözdemir is a Research Assistant at the Department of Mining Engineering, Istanbul Technical University (ITU), with expertise in production planning, geological modeling, and operations research. He holds a PhD in Mining Engineering from ITU (2019–2024), following a Master's (2017–2019) and Bachelor's (2014–2017) in the same field. His research focuses on optimizing open-pit mining operations through advanced methodologies like computer vision, multi-objective programming, and parametric analysis. His work addresses challenges in fleet management, production scheduling, and resource estimation, contributing to sustainable mining practices aligned with UN SDGs. Notable research includes real-time truck dispatching systems and enhanced integer programming models for long-term mine planning. Hasözdemir's publications span topics from injection foam optimization for material strengthening to pit limit analysis. He collaborates internationally and maintains active profiles on Scopus, Google Scholar, and ResearchGate.

Ramazan Çağlarمشاهده پروفایل

دانشیار

• Electric Power Distribution
• Reliability Assessment
• System Reliability
• +۱۱ مورد دیگر

Ramazan Çağlar is an Associate Professor in the Department of Electrical Engineering at Istanbul Technical University, College of Engineering. His research focuses on modern power systems, including microgrids, renewable energy integration, reliability assessment, and intelligent control of distributed energy resources. He actively publishes in high-impact journals and leads research initiatives in smart grid technologies. Research Interests: His work spans electric power distribution, system reliability, microgrid dynamics, induction motors, and power transmission. He integrates advanced computational methods such as machine learning, Bayesian inference, and optimization algorithms to solve complex problems in energy systems. His recent focus includes fault prediction using drones, energy forecasting with neural networks, and optimal allocation of distributed generators. Recent Research Trends: Analysis of his latest publications (2022–2024) reveals a strong trend toward data-driven and AI-enhanced modeling in microgrids and renewable integration. He combines physical models with machine learning (e.g., neural ODEs, autoencoders, LSTMs) and applies multi-objective optimization techniques like multiverse optimization. His work increasingly emphasizes uncertainty quantification, real-time control, and sustainable energy solutions. Scientific Projects: Completed: Reliability Evaluation of Power Station Designed for DC-Fed Traction Systems in Light Rail Transit (2011–2021). Advising and Grants: He is currently supervising 14 theses in progress, indicating an active role in mentoring graduate students. While specific grant details are limited, his long-running project funded by ITU's BAP (Scientific Research Projects) suggests sustained research funding. His collaborations include international researchers, particularly in Africa and the Middle East. Labs and Research Teams: Though not explicitly named, his research activities suggest leadership in a power systems and smart grid laboratory at ITU, focusing on reliability, optimization, and AI applications in energy infrastructure.

Emre ÇANKAYAمشاهده پروفایل

استادیار

• Optimization
• Health and Disaster Logistics
• Production and Inventory Planning
• +۳ مورد دیگر

Dr. Emre ÇANKAYA serves as an Assistant Professor in the Department of Industrial Engineering at Doğuş University's Faculty of Engineering, specializing in mathematical optimization and logistics systems. His academic journey spans doctoral research at Özyeğin University and professional experience at TÜBİTAK/TÜSSİDE. His educational credentials include: Ph.D. in Industrial Engineering, Özyeğin University (2022) Master's in Industrial Engineering, Özyeğin University (2017) Bachelor's in Industrial Engineering, Özyeğin University (2013) ÇANKAYA's research integrates mathematical modeling with practical applications in high-stakes domains. His work bridges theoretical optimization techniques and real-world implementation challenges, particularly in disaster response systems and industrial production environments. Methodologically, he specializes in developing heuristic algorithms for NP-hard problems while maintaining computational efficiency. Publication analysis reveals a consistent focus on combinatorial optimization with dual applications: manufacturing processes (label printing logistics) and humanitarian operations (disaster relief). His research demonstrates sophisticated adaptation of inventory routing frameworks to volatile environments, showing particular innovation in multi-phase heuristic development for time-sensitive supply chains. Teaching responsibilities encompass core industrial engineering subjects including Simulation Modeling, Supply Chain Management, and Operations Research, delivered primarily in English to international student cohorts. His industry experience at Kumport Port Services informs practical case studies in transportation network design.

• Automotive Engineering
• Internal Combustion Engines
• Diesel Engine Optimization
• +۵ مورد دیگر

Prof. Muammer ÖZKAN serves as Professor and Head of the Department of Mechanical Engineering at Yıldız Technical University's Faculty of Mechanical Engineering. He has held academic positions since 1990, progressing from Research Assistant to his current Professorship since 2015, with additional leadership as Head of Department since 2016. Doctorate in Mechanical Engineering, Yıldız Technical University (1992-1997) Postgraduate in Mechanical Engineering, Yıldız Technical University (1990-1992) Undergraduate in Mechanical Engineering, Yıldız Technical University (1984-1989) Prof. ÖZKAN's research focuses on automotive engineering with specialization in internal combustion engines, particularly diesel engine optimization, emission control, and fuel efficiency. His work extensively covers vehicle dynamics, brake systems, and energy balance analysis, with significant contributions to understanding the effects of multiple injection strategies on engine performance and emissions. He has pioneered research on biodiesel applications and developed advanced modeling techniques for public transportation systems. His publication record shows consistent research output with 15 recent articles (2020-2024) demonstrating strong focus on emission reduction strategies, low-temperature combustion techniques, and optimization of public transportation systems. His work combines experimental investigation with advanced computational modeling, including artificial neural networks and 3D road geometry approaches for fuel consumption modeling. Prof. ÖZKAN has supervised 33 theses (doctoral and postgraduate) and led numerous research projects, including TÜBİTAK-funded initiatives on diesel engine modernization and Euro6 heavy-duty engine development. His work has significant practical applications in urban transportation emissions reduction and sustainable vehicle technologies.

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.

Elvin Çobanمشاهده پروفایل

دانشیار

• Healthcare and Humanitarian Operations
• Optimization
• Service Systems
• +۳ مورد دیگر

Elvin Çoban serves as Associate Professor and Chair of the Industrial Engineering Department within Ozyegin University's Faculty of Engineering, specializing in optimization applications for healthcare systems, humanitarian logistics, and service operations. Her research bridges theoretical operations research with real-world implementation in critical infrastructure domains. Education Background: PhD in Operations Management and Manufacturing, Carnegie Mellon University (2012) MS in Operations Management and Manufacturing, Carnegie Mellon University (2010) MS in Industrial Engineering, Sabanci University (2008) BS in Manufacturing Systems Engineering, Sabanci University (2006) Dr. Çoban's research program focuses on developing mathematical models for complex decision-making under uncertainty, with significant contributions to disaster response logistics using drone technology and healthcare operations management. Her work integrates machine learning with combinatorial optimization to solve scheduling problems in medical facilities and vaccine distribution networks, while also exploring energy systems through electric vehicle charging infrastructure modeling. Current projects emphasize predictive analytics for post-disaster damage assessment and resource allocation during pandemics. Analysis of her 15 most recent publications (2019-2024) reveals three dominant research thrusts: (1) humanitarian logistics (35% of output), particularly drone routing for disaster assessment; (2) healthcare operations (50%), including operating room scheduling and vaccine distribution; and (3) energy systems (15%), focused on electric vehicle infrastructure optimization. Methodologically, her work combines stochastic programming, metaheuristics, and machine learning to address data-scarce environments. Research Funding and Advisory: Principal Investigator for TUBITAK 1001 project (2021-2024): Information Gathering and Damage Prediction for Post-Disaster Drone Assessment Researcher for TUBITAK 3501 project (2013-2016): Blood Supply Chain Optimization Consultant for TEA Networks (2018-2021) and TUBITAK 1507 workforce management project (2014-2016) Advises three graduate students: Kian Farajkhah (PhD candidate), Birce Adsanver (MSc 2020), and Gulsah Alper (MSc 2015) on optimization applications in disaster response and healthcare She leads an active research group at Ozyegin University that collaborates with government agencies and industry partners on projects involving real-time decision support systems. Current initiatives focus on integrating real-world constraints into optimization models for medical logistics and developing scalable algorithms for drone-based disaster assessment networks, with ongoing recruitment for TUBITAK-funded humanitarian logistics research positions.

• Computer Science
• Wireless Sensor Networks
• Network Protocols
• +۷ مورد دیگر

Assoc. Prof. Selcuk Okdem is a faculty member at Erciyes University, Faculty of Engineering, Department of Computer Engineering, specializing in Computer Hardware. With academic ranks from Research Assistant (2001-2011) to Assistant Professor (2011-2017) and current Associate Professor (2017-present), his career spans 25+ years in academia and industry. Doctorate : 2004-2011, Erciyes University, Computer Engineering Postgraduate : 2001-2003, Erciyes University Undergraduate : 1996-2001, Erciyes University His research focuses on Wireless Sensor Networks , Optimization Algorithms , and Embedded Systems , with notable work on AI integration in cybersecurity and evolutionary optimization techniques. His 15 most recent publications (2024-2004) span topics like genetic algorithm scheduling , neural networks for localization , and cross-layer protocol design . Scientific achievements include: 2021: Certificate of Honor for Top Graduation Term 1999-2000: Turkish-Japanese Foundation Scholarship He has served as peer reviewer for SCI journals (Wireless Networks, Applied Soft Computing) and participated in 15+ international conferences (IEEE CEC, INISTA, IWCMC). His managerial role since 2015 includes Erciyes University leadership positions.

İhsan Hakan SELVİمشاهده پروفایل

استاد

• Manufacturing Systems
• Production Systems
• Artificial Intelligence in Manufacturing
• +۵ مورد دیگر

Prof. Dr. İhsan Hakan SELVİ is a distinguished Professor at Sakarya University's Faculty of Computer and Information Sciences, Department of Information Systems Engineering. With a career spanning over two decades at Sakarya University, he has progressed from Research Assistant positions (2001-2017) to his current professorship. His administrative leadership includes serving as Institute Director of the Institute of Science (2022), Deputy Head of Department (2016-2020), and multiple board memberships including the Sakarya Economic and Social Research Center (2023-2026). His research expertise spans manufacturing systems, particularly Seru production systems, assembly systems, deep learning applications in manufacturing, and Industry 4.0 technologies. His work bridges traditional industrial engineering with modern AI techniques, focusing on practical applications in production environments. He has developed conceptual models for smart seru production systems using deep learning for real-time monitoring and controlling, and has conducted extensive bibliometric analyses of production scheduling problems. Prof. SELVİ's scholarly output demonstrates strong trends in applying AI to manufacturing challenges, with increasing focus on deep learning approaches in recent years. His work shows interdisciplinary connections between industrial engineering, computer science, and operations research. His publications span high-impact journals in production research, computer-integrated manufacturing, and industrial engineering. Bilim Ödülü (Science Award) Teşekkür Belgesi (Certificate of Appreciation) As an academic advisor, he has supervised numerous graduate students across diverse topics including machine learning for anomaly detection in water consumption, protein structure prediction using deep learning, and automotive plant logistics optimization. His research projects include significant collaborations such as the "Kauçuk Hamuru tasarımı ve üretimi" project focusing on rubber compound design and Industry 4.0 applications in the rubber sector. He has also contributed to knowledge management and digital transformation through various book chapters and edited volumes. Prof. SELVİ leads or participates in multiple research teams focusing on production systems optimization, AI applications in manufacturing, and digital transformation. His current work continues to explore the intersection of deep learning and manufacturing systems, particularly in the context of Industry 4.0 implementation in Turkish industrial settings.

Nilüfer YURTAYمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Artificial Intelligence
• +۷ مورد دیگر

Prof. Dr. Nilüfer YURTAY is a distinguished faculty member at Sakarya University's Faculty of Computer and Information Sciences, Department of Computer Engineering. Her academic career spans over three decades, beginning as a Research Assistant in 1992 and advancing to her current position as Professor. Current Courses: Computer Engineering Design, Optimization, Medical Statistics and Medical Informatics, Graduation Projects, Data Mining Applications, and Specialization Areas Previously Taught: Discrete Operational Structures, Data Mining, Applied Engineering Experience Training Professor YURTAY's research spans multiple domains of computer science with a focus on practical applications. Her work demonstrates consistent excellence in machine learning, data mining, and artificial intelligence, particularly applied to medical informatics, traffic safety analysis, and assistive technologies for visually impaired individuals. She has made significant contributions to feature selection algorithms, metaheuristic optimization techniques, and medical diagnosis systems. Her publication record reveals a strong trajectory of high-impact research, with recent works focusing on real-time object detection (AYOLO), traffic accident analysis using decision trees, binary chaotic optimization algorithms, and voice-assisted systems for visually impaired students. These publications appear in reputable journals including IEEE ACCESS, Applied Sciences, and Engineering Science and Technology. Professor YURTAY has supervised numerous graduate students, guiding research in data mining applications in healthcare, Turkish text-to-speech synthesis, pattern recognition in bioinformatics, and demand prediction for ATMs. Her academic leadership extends to developing educational resources, including her textbook 'Ayrık İşlemsel Yapılar' (Discrete Operational Structures). Her research group maintains active projects in medical informatics, traffic safety analysis, and assistive technologies, reflecting her commitment to solving real-world problems through computational methods. Professor YURTAY's work bridges theoretical computer science with practical applications that address societal challenges in healthcare, transportation, and accessibility.

Muhammed Kotanمشاهده پروفایل

استادیار

• Artificial Intelligence
• Computer Software
• Image Processing
• +۱۳ مورد دیگر

Muhammed Kotan is an Assistant Professor at the Department of Information Systems Engineering , Sakarya University , where he has served since 2022. His academic career includes roles as a Research Assistant (2011–2022) at Sakarya University and Afyon Kocatepe University. He holds a PhD in Computer and Information Engineering (2020), an MSc in Computer and Information Engineering (2014), and a BSc in Computer Engineering (2011) from Sakarya University. Research Interests : Artificial Intelligence, Computer Software, Image Processing, Machine Learning, Computer Vision, Medical Imaging, Energy Efficiency Optimization, Natural Language Processing, Sentiment Analysis, Feature Selection, 3D Reconstruction, Industrial Machine Detection, Optimization Algorithms, Real-Time Systems, User Reviews Analysis, E-Commerce Analytics. Key Contributions : Developed hybrid methods for 3D reconstruction and industrial machine defect detection, optimized tow train routing in manufacturing, and applied Marine Predators Algorithm for mental health screening. Scientific Recognition : Awarded the Eğitim Öğretimde Üstün Başarı Ödülü by Sakarya University (2024). Served as editor for the Sakarya University Journal of Computer and Information Sciences (2023–2024) and peer reviewer for journals like Signal, Image and Video Processing and Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi . Advising : Co-advisor for TÜBİTAK projects (2022–2024) and supervised student research on topics like cosmetic product recognition, natural language processing, and sentiment analysis. Designed courses in Digital Image Processing, Text Mining, and Advanced Information Systems.

Sümeyye KAYNAKمشاهده پروفایل

استادیار

• Artificial Intelligence
• Cloud Computing
• Environmental Informatics
• +۳ مورد دیگر

Sümeyye KAYNAK serves as an Assistant Professor in the Department of Computer Engineering at Sakarya University's Faculty of Computer and Information Sciences. Her academic profile demonstrates interdisciplinary expertise bridging computer science with environmental sustainability and educational technology through computational modeling and data-driven solutions. Her educational foundation includes: Doctorate (2019) from Sakarya University Institute of Science/Computer and Information Engineering with thesis on solar energy potential modeling using 3D data Master's degree (2014) from same institution focusing on AI-based student counseling infrastructure Bachelor's degree (2012) in Computer Engineering from Sakarya University Faculty of Engineering Dr. KAYNAK's research integrates advanced computational methodologies across multiple domains. Her primary focus areas include Artificial Intelligence applications for resource allocation in cloud manufacturing (utilizing genetic algorithms and AHP), Environmental Informatics for flood modeling and hydrological analysis, and Sustainable Energy systems through solar potential estimation tools. Recent work demonstrates significant expansion into geospatial web components for earth science agencies and digital twin frameworks for urban infrastructure resilience, reflecting growing emphasis on climate change adaptation technologies. Publication analysis from 2012-2025 reveals a clear research evolution: early work (2012-2018) concentrated on educational AI systems and solar energy tools, while current output (2023-2025) prioritizes environmental applications with city-scale flood impact modeling, hydroinformatics, and open data frameworks. This trajectory shows increasing sophistication in real-time data analytics and cross-domain integration, particularly between manufacturing systems and environmental monitoring. No scientific awards were documented in the source materials. Regarding academic mentoring, the available documentation contains no references to graduate student supervision or grant-funded research projects. No laboratory affiliations or research team leadership roles were specified in the provided information.

• Quality Management
• Production and Service Systems Management
• Reliability Engineering
• +۲ مورد دیگر

Prof. Dr. Sezgin Çağlar Aksezer is a Full-Time Faculty Member in the Department of Industrial Engineering at Isik University's College of Engineering and Natural Sciences. His academic career spans over two decades, focusing on quality management, production systems, reliability analysis, and optimization techniques. PhD in Industrial Engineering (2003), Northeastern University MS in Industrial Engineering (2000), Northeastern University BS in Mathematics Engineering (1999), Yıldız Technical University His research emphasizes industrial systems optimization, particularly in corrective maintenance strategies, warranty modeling, and probabilistic graphical models for multi-component systems. He applies advanced statistical methods and dynamic frontier analysis to healthcare services and retail operations, bridging theoretical frameworks with real-world applications in thermal power plants, consumer electronics, and hospital efficiency. Key trends in his work include: Development of probabilistic graphical models for maintenance optimization Integration of Bayesian networks in complex system diagnostics Application of Data Envelopment Analysis (DEA) to evaluate institutional performance Exploration of Kano models and desirability functions for quality control He has supervised several graduate theses, including studies on credit evaluation processes, football league predictions, and customer expectation measurement methodologies. His teaching portfolio includes courses in Quality Planning, Production Operations, Reliability Analysis, and Applied Statistics since 2012.

• Modeling and Optimization
• Operations Research
• Simulation

Seda Baş Güre is a full-time Lecturer in the Department of Industrial Engineering at Işık University's Faculty of Engineering and Natural Sciences, where she has taught since at least 2017. Her expertise bridges industrial engineering theory with healthcare system optimization, focusing on practical applications in resource-constrained medical environments. Her academic credentials include: PhD in Systems Engineering, Yeditepe University (2011-2018) Master's in Business Administration, Yeditepe University (2008-2011) Bachelor's in Systems Engineering, Yeditepe University (2003-2008) Dr. Baş Güre's research centers on modeling and optimization within healthcare logistics, particularly blood donation scheduling and pharmacy assignment systems. Her work employs stochastic programming and simulation to address uncertainty in donor behavior and resource allocation, contributing to more resilient blood supply chains. She emphasizes real-world applicability, with case studies in Istanbul healthcare facilities. Her publication trajectory from 2013-2020 reveals deepening specialization in blood donation systems, evolving from cognitive automation frameworks to sophisticated risk-averse scheduling models. The research consistently targets operational bottlenecks in blood collection, balancing donor convenience with blood unit production efficiency through two-stage optimization approaches. As an educator, Dr. Baş Güre mentors graduate students while teaching core industrial engineering courses including System Simulation and Scheduling in both English and Turkish. Her curriculum development reflects her research focus, integrating healthcare case studies into traditional operations research coursework.