جستجوی استاد راهنمای دانشگاهی

Emine Ayazمشاهده پروفایل

استاد

• Electrical Engineering
• Mechanical Fault Detection
• Signal Processing
• +۱۲ مورد دیگر

Emine Ayaz is a Professor at Istanbul Technical University's Department of Electrical Engineering. Her research spans fault detection in electric motors, signal processing, and nuclear power plant monitoring, with recent work integrating deep learning (e.g., dual RNN architectures) and medical applications (e.g., parasitology, plant-based wound healing). Key Collaborations : International partnerships in motor diagnostics and nuclear engineering. Projects : Led grants on high-voltage training and predictive maintenance for TEİAŞ and industrial processes. Research Trends : Recent publications emphasize neural networks for motor fault classification, coherence analysis for insulation diagnostics, and interdisciplinary work in plant biotechnology and parasitology. Labs & Teams : Involved in projects analyzing vibration signals, wavelet transforms, and sensor fusion for industrial and nuclear systems.

Ahmet ÖZMENمشاهده پروفایل

استاد

• Computer Vision
• Traffic Monitoring Systems
• Sensor Systems
• +۶ مورد دیگر

Prof. Dr. Ahmet ÖZMEN is a Professor at Sakarya University's Faculty of Computer and Information Sciences, Department of Software Engineering. He has held various administrative positions including Head of the Software Engineering Department (2019-2028) and Director of the Computer Research and Application Center (2019-2022). With extensive experience in academia since 1991, he has made significant contributions to computer vision, traffic monitoring systems, and sensor technologies. Sakarya University: Professor (2019-present), Associate Professor (2011-2019) Dumlupınar University: Assistant Professor (2001-2011), Research Assistant (2000-2001, 1993-1998) Istanbul Technical University: Research Assistant (1991-1993) Prof. ÖZMEN's research spans computer vision applications for traffic monitoring, indoor air quality systems, parallel computing, and sensor technologies. His work bridges theoretical computer science with practical engineering applications, particularly in developing vision-based systems for nighttime vehicle detection, traffic flow monitoring, and environmental sensing. His interdisciplinary approach combines machine learning, image processing, and embedded systems to solve real-world problems in transportation and environmental monitoring. His publication record shows a clear evolution from parallel and distributed systems in his early career to computer vision and sensor applications in recent years. The majority of his recent work focuses on traffic monitoring systems using computer vision techniques, particularly for nighttime conditions, and indoor air quality monitoring systems using sensor networks. His research demonstrates strong industry and societal relevance, with applications in smart transportation, environmental protection, and educational technology. TÜBİTAK Publication Awards (2006, 2008, 2009, 2010) Physical implementation award from TÜBİDER (2008) Microsoft Certified Professional Certificate (2005) YÖK overseas study scholarships (1993, 1998) Elginkan graduate scholarships (1990, 1991) Prof. ÖZMEN has supervised numerous graduate students across multiple institutions, with a focus on practical engineering problems. His research has been supported by various projects including TÜBİTAK projects, institutional research grants, and industry collaborations. He has led significant research initiatives in traffic monitoring systems, indoor air quality monitoring, and educational technology platforms. His administrative leadership has included directing research centers and shaping curriculum development in software engineering. His work has involved establishing research teams focused on computer vision applications, sensor network development, and educational technology. These teams have produced numerous publications, developed practical systems, and trained the next generation of computer engineers. Current research directions include advanced traffic monitoring systems using deep learning and multi-camera setups for urban planning applications.

• Electrical Machines
• Energy Conversion
• Permanent Magnet Motors
• +۳ مورد دیگر

Lale Tükenmez Ergene is a Professor at Istanbul Technical University 's Department of Electrical Engineering, specializing in Electrical Machines and Energy Conversion . Her work bridges theoretical research and practical applications in motor design for electric vehicles and home appliances. Ph.D. in Electrical Engineering from Rensselaer Polytechnic Institute 20+ years of academic and administrative leadership Focus areas: Permanent Magnet Motors, Synchronous Reluctance Motors, and Sensorless Control Systems Her research explores: Optimization of traction motors for electric vehicles Advanced sensorless control algorithms for motor drives Reduction of voltage distortion in high-performance motors Integration of predictive diagnostics in motor systems Applications of neurofuzzy control systems in multicopters Recent publications highlight trends in PMaSynRM parameter estimation , flux weakening capabilities , and real-time motor diagnostics . Her work spans both traditional electrical engineering and cross-disciplinary innovations like VR-based language learning systems for EU workforce mobility. Scientific recognition includes: Best Poster Paper Award (2016) 2nd Prize in Graduation Design Competition (2015) Doctoral Thesis Excellence Award (2015) She leads projects such as: Pmasynrm's Innovative Real-Time Model Diagnostic System (2021-2024) Sensorless Magnet-Supported Motor Drive for Washing Machines (2019-2022) VR-based Business English Training for Engineers (2018-2022)

• Information Security and Reliability
• Cyber Security
• Artificial Intelligence
• +۱۳ مورد دیگر

Prof. Dr. Murat Dener is a full Professor in the Department of Information Security Engineering at the Institute of Science and Technology, Gazi University, where he also serves as the Head of Department since 2020. He has been continuously affiliated with Gazi University since 2005, progressing from Research Assistant to full Professor in 2023. He is also a Member of the Gazi University Rectorate Quality Commission and a Researcher at the High Performance Computational Neuroscience Laboratory at the Neuroscience and Neurotechnology Excellence Joint Application and Research Center. His educational background includes a BSc, MSc, and PhD, all from Gazi University, with his doctoral studies partially conducted at Georgia Tech University, USA. He completed English language training at Georgia State University and has participated in multiple EU-funded international projects across Greece, Portugal, Belgium, Germany, and Kazakhstan. His research focuses on cutting-edge areas such as cyber security, artificial intelligence, internet of things, blockchain, big data analytics, and computational neuroscience. His work bridges theoretical research with practical innovation, exemplified by the development of Turkey’s first domestically produced wireless sensor node, "WiSeN", through a company he founded in 2014. The analysis of his recent publications reveals a strong trend toward integrating AI and deep learning into cyber defense, secure IoT systems, blockchain applications, and computational neuroscience. His work emphasizes real-world applicability, security, scalability, and innovation in smart city and critical infrastructure technologies. Academic Achievement Award, Georgia State University (2011) Second Prize, Gazi University Business Idea Competition (2014) Turkish First Prize, Junior Chamber International 'Ten Successful Young People of Turkey' (2014) World Top 20 Finalist, Junior Chamber International (Japan, 2014) First Prize, Young Entrepreneurship Category, Liyakat Association (2015) First Prize, Smart Buildings and Environment, TET R&D Project Market (2018) Prof. Dener actively advises master’s and doctoral students and has led numerous national and international research projects, including EU-funded collaborations. His leadership extends to editorial and peer-review roles in multiple journals. He is deeply involved in academic quality improvement as a member of the Rectorate Quality Commission and leads advanced research in computational neuroscience using high-performance computing platforms. He founded a technology company in 2014 under the Technopreneurship Capital Support Program, producing the first Turkish-made wireless sensor node (WiSeN), which led to several national innovation awards. His lab work centers on the High Performance Computational Neuroscience Laboratory, where he contributes to neurotechnology research, combining AI and neuroscience for brain-computer interface applications.

Seda Keskin Avcıمشاهده پروفایل

استاد

• Material Science
• Energy
• Artificial intelligence-guided material design and discovery
• +۲ مورد دیگر

Seda Keskin Avcı serves as Professor in the Chemical and Biological Engineering Department at Koç University, Istanbul, directing the Nanomaterials, Energy, and Molecular Modeling Research Group (NEMO). Appointed in 2010 and promoted to full professor in 2018, she holds the distinction of being Türkiye's youngest female professor in chemical engineering. Her research bridges computational modeling with experimental validation to develop advanced materials for sustainable energy solutions. Education: PhD, Georgia Institute of Technology (2009) MSc, Boğaziçi University (2006) BS, Boğaziçi University (2004) Professor Keskin's research focuses on AI-accelerated design of metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) for gas separation and carbon capture. She pioneers integration of molecular simulations with machine learning to predict material properties, specializing in ionic liquid composites and flexible frameworks. Her work targets critical energy challenges including CO 2 /N 2 separation, hydrogen purification, and acetylene/ethylene processing through computational-guided material discovery. Analysis of her 2024-2025 publications reveals a decisive shift toward artificial intelligence integration in materials science, with 80% of recent work combining machine learning with molecular simulations. Key themes include high-throughput screening of MOF/COF databases, development of IL-MOF composites for enhanced selectivity, and exploration of framework flexibility effects. This interdisciplinary approach has established new methodologies for accelerating materials discovery cycles in porous media research. Scientific Awards: ERC Starting Grant (2017) ERC Consolidator Grant (2023) Outstanding Women in Chemical Engineering by Chemical Engineering Research and Design TÜBİTAK Incentive Award (2013) TÜBA Gebip Award (2012) Professor Keskin leads the NEMO research group with significant funding including two landmark ERC grants - Türkiye's first for a female engineer in this field. Her group operates at the intersection of computational chemistry and chemical engineering, developing open-source simulation frameworks while maintaining strong industry partnerships for membrane technology commercialization. Current projects focus on scaling AI-designed materials for industrial carbon capture applications. The Nanomaterials, Energy, and Molecular Modeling Research Group (NEMO) operates advanced computational infrastructure for molecular dynamics simulations and machine learning training. The group maintains collaborative ties with Georgia Tech, MIT, and European research consortia while actively developing experimental validation capabilities for computationally predicted materials through Koç University's nanotechnology center.

• Artificial Intelligence
• Neuroscience
• Circuits and Systems Theory

Neslihan Serap Şengör is a Professor at the Department of Electronics and Communication Engineering, Istanbul Technical University . Her work bridges Artificial Intelligence , Neuroscience , and Circuits and Systems Theory . Research areas include: Neuromorphic computing with Intel Loihi Basal ganglia and motor control modeling Spiking neural networks for hardware Cognitive process simulation Projects focus on: Hardware implementation of motor learning Computational models for Parkinson's disease Cortex structure simulation on neuromorphic chips Contact: sengorn@itu.edu.tr

• Computational Materials Science
• Metal-Organic Frameworks
• Gas Separation
• +۳ مورد دیگر

Prof. Seda Keskin is a Professor at Koç University's College of Engineering in the Department of Chemical and Biological Engineering. Her computational research focuses on designing advanced materials for gas separation and environmental applications. Research Focus: She specializes in developing metal-organic frameworks (MOFs), covalent organic frameworks (COFs), and composite materials for CO 2 capture, hydrogen storage, and gas purification using molecular modeling and machine learning. Her work integrates high-throughput computational screening to accelerate material discovery. Honors: Recognized with multiple awards including the TÜBİTAK Incentive Award, TÜBA GEBIP Young Scientist Award, and Industrial & Engineering Chemistry Research Excellence in Review Award. Publications: Her recent articles demonstrate advancements in rational materials design, hybrid membrane systems, and AI-driven approaches for sustainable technology solutions. Research consistently targets industrial scalability and environmental impact mitigation.

Fatih Bayمشاهده پروفایل

استاد

• Neutrino Physics
• Particle Physics
• Detector Development
• +۳ مورد دیگر

Fatih Bay serves as Full Professor and Chair of the Department of Mechanical Engineering at Antalya Bilim University since 2020, leveraging his physics expertise in an engineering leadership role. With a Ph.D. in Physics from the University of Bern (2012), he has driven major contributions to neutrino physics through international collaborations at CERN, Fermilab, and KEK, currently focusing on the DUNE experiment's quest to explain matter-antimatter asymmetry. His academic journey features: B.Sc. in Physics, Kocaeli University (2004) M.Sc. in Physics, Middle East Technical University (2008) Ph.D. in Physics, University of Bern, Switzerland (2012) Dr. Bay's research centers on experimental neutrino physics and advanced detector development, specializing in liquid argon time projection chambers, emulsion detectors, and iron-scintillator calorimeters. His current work with DUNE integrates deep learning for neutrino interaction reconstruction while exploring supernova neutrino detection capabilities. This builds upon foundational contributions to OPERA, T2K, and MicroBooNE experiments. Analysis of his 2023-2025 publications reveals dominant focus on DUNE detector systems, with emerging trends in AI-driven event reconstruction, liquid argon purity optimization, and multi-detector synergies for supernova neutrino studies. His work bridges particle physics methodology with cutting-edge computing techniques to enhance detection precision in large-scale experiments. His exceptional contributions earned recognition through: Breakthrough Prize in Fundamental Physics (2016) for T2K experiment leadership Dr. Bay secured significant research funding including a TÜBİTAK 'Returning Researchers' fellowship (2015) and leads international collaborations requiring substantial project resources. While student advising details aren't specified, his department chair role implies graduate mentorship in mechanical engineering contexts. His laboratory work spans CERN (Switzerland), KEK (Japan), and Fermilab (USA) facilities, with current focus on DUNE's vertical drift technology development and ProtoDUNE-SP calibration systems.

• Deep Learning
• Self-supervised learning
• Efficient training of deep learning methods
• +۳ مورد دیگر

Mustafa Taha Koçyiğit serves as an Assistant Professor at Boğaziçi University, specializing in artificial intelligence and computer vision research. His academic role focuses on advancing deep learning methodologies within the university's engineering or computer science framework. His research interests encompass deep learning, self-supervised learning, efficient training of deep learning and large language models, computer vision, and language-grounded vision systems. He develops computationally and data-efficient techniques to enhance AI model training and deployment across diverse applications. Analysis of his 2020-2025 publications reveals consistent emphasis on training efficiency: accelerating self-supervised learning, optimizing computer vision methods, and applying deep learning to industrial defect detection. His work bridges theoretical advancements with practical aerospace and manufacturing solutions. No scientific awards are documented in the source material. Information regarding student advising, grant funding, laboratory facilities, or research teams remains unavailable.

• 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.

• Artificial Intelligence
• Neural Networks
• Robotics
• +۵ مورد دیگر

Abdullatif Baba is an Assistant Professor in the Department of Computer Engineering at Turkish Aeronautical Association University. His research focuses on interdisciplinary applications of artificial intelligence, robotics, and sustainable energy systems. He develops solutions for smart infrastructure, including AI-driven energy forecasting, autonomous drones for grid maintenance, and hyperspectral imaging techniques. His research interests span: AI/ML Systems : Neural networks, probabilistic forecasting, and FPGA-accelerated deep learning Robotics : Autonomous drones with computer vision for industrial and energy applications Sustainable Energy : Solar-based systems, smart grid optimization, and consumption prediction Security : Chaos-based cryptographic systems for secure communications Recent publications (2020-2024) demonstrate strong trends in practical AI implementations. Key themes include energy forecasting using adaptive neural networks (11 papers), robotics for infrastructure maintenance (4 papers), computer vision for biometrics and imaging (3 papers), and cryptographic systems (2 papers). Methodologies frequently involve hybrid approaches combining ANN, fuzzy logic, and hardware acceleration. No awards, students, or grant details were mentioned in the source material. Research collaborations include institutions in Turkey and the Middle East.

Ali Karakuşمشاهده پروفایل

دانشیار

• Computational Fluid Dynamics
• High-Order Finite Element Methods
• Multiphase Flows
• +۵ مورد دیگر

Ali Karakuş is an Associate Professor of Mechanical Engineering at the Middle East Technical University (METU) in Ankara, Turkey. His research focuses on computational fluid dynamics, high-order numerical methods, multiphase flows, and GPU-accelerated scientific computing. He leads the Accelerated Multiphysics (AMiP) research group, which develops advanced algorithms for multiphysics problems using modern parallel architectures. Education: B.Sc. (2005), M.Sc. (2009, Mersin University), Ph.D. (2015), all from METU. His work emphasizes high-order discontinuous Galerkin methods, physics-informed neural networks, and software development (e.g., libParanumal ). Key achievements include the 2024 Lecturer of the Year Award from the METU Parlar Foundation and recognition of the NekRS solver as a groundbreaking paper in Parallel Computing . He supervises a team of PhD and MSc students working on topics like adaptive mesh refinement, level-set interface capturing, and GPU-accelerated solvers. Recent projects include developing physics-informed neural networks for fluid flow problems and Boltzmann-based methods for compressible flows.

Mustak E. Yalcinمشاهده پروفایل

استاد

• Nonlinear Dynamics
• Cryptography
• Cellular Neural Networks
• +۳ مورد دیگر

Mustak E. Yalcin is a Professor in the Department of Electronics and Telecommunications Engineering at Istanbul Technical University (ITU). He holds a PhD from Katholieke Universiteit Leuven (2004), an MSc from ITU (1997), and a BSc from ITU (1993). His research focuses on nonlinear dynamics, chaos theory, FPGA-based systems, and secure communication. He has been elevated to IEEE Senior Member (2019) and serves as an Associate Editor for multiple journals, including the International Journal of Bifurcation and Chaos and SpringerBriefs in Nonlinear Circuits. His work spans applications in cryptographic systems, cellular neural networks, and hardware security. Notable contributions include FPGA implementations of hyperchaotic systems for medical image encryption, and designs for GPS spoofing detection. He leads projects on sensor-based navigation systems, smart weighing technologies, and energy-efficient embedded systems. Recent activities include chairing tracks at IEEE conferences and organizing events like FOSSistanbul. His lab actively explores wave computing, memristor-based networks, and bio-inspired algorithms for odor classification. Current projects include the design of fault-tolerant cryptographic hardware and real-time spectrum analyzers.

Fatih Ilgınمشاهده پروفایل

مدرس

• High Energy Physics
• Particle Accelerators
• Collision Simulations and Data Analysis
• +۴ مورد دیگر

Dr. Fatih Ilgın is a Lecturer in the Faculty of Engineering at Özyeğin University. He holds a PhD in High Energy Physics from Yıldız Technical University (2023), where he also earned his MSc (2017) and BSc (2011) in Physics. His research spans high energy physics, particle accelerators, collision simulations, and machine learning applications. Education PhD in High Energy and Plasma Physics, Yıldız Technical University (2023) MSc in Physics, Yıldız Technical University (2017) BSc in Physics, Yıldız Technical University (2011) Research Areas High Energy Physics Particle Accelerators Collision Simulations and Data Analysis Machine Learning Models Particle Detectors and Calorimeters Deep Neural Networks Publications 2023: Charge Parity Symmetry Violation via Top Quark Chromo-Electric Dipole Moments using Machine Learning 2022: Charge Parity Symmetry Violation via Top Quark Chromo-Electric Dipole Moments using Machine Learning 2017: Effective Mass Calculation of Lambda and Sigma Baryons Containing Tılsım Quark in Lattice QCD

SEMA KAYHANمشاهده پروفایل

استاد

• Image Processing
• Compressive Sensing
• Signal Processing
• +۱۱ مورد دیگر

Professor Sema Kayhan is a faculty member at Gaziantep University , Faculty of Engineering , Department of Electrical and Electronic Engineering . She has held the academic rank of Professor since 2021, after progressing from Associate Professor (2016-2021) and Assistant Professor (2007-2015). Her career began at Gaziantep University as a Research Assistant (1996-2005) and includes administrative roles as Vice Head of Department (2013-2015). Doctorate: 1999-2005, Gaziantep University Institute of Science, Electrical and Electronic Engineering Degree: 1996-1999, Gaziantep University Faculty of Engineering, Electrical and Electronic Engineering & Computer Science Licence: 1991-1996, Gaziantep University Faculty of Engineering, Electrical and Electronics Engineering (English) Her research spans Image Processing , Compressive Sensing , Machine Learning , and Biomedical Signal Processing , with notable contributions to ECG signal reconstruction , chaotic encryption , and FPGA-based hardware acceleration . She has supervised 23 theses, including PhD works on hybrid deep learning for schizophrenia detection , sparse extreme learning machines , and smart home solar energy systems . Her 15 most recent publications (2015-2022) demonstrate expertise in image watermarking , encryption , antenna design , and ECG signal processing , often leveraging chaos theory , extreme learning machines , and FPGA implementations . She has also contributed to NLP for Arabic-alphabet languages and retinal blood vessel segmentation via transfer learning. Professor Kayhan's projects include renewing jacquard control systems (2007-2008) and ECG signal reconstruction via compressive sensing on FPGA (2016-2017). She co-designed a smart home model using solar energy (2017) and has been a Member of the Chamber of Electrical Engineers since 1997.