جست‌وجوی استاد و پژوهشگر

Evin Şahin Sadıkمشاهده پروفایل

مدرس

• Biomedical Systems
• Biomedical Sensors
• EEG and Signal Processing
• +۲ مورد دیگر

Dr. Evin Şahin Sadık is a Lecturer at Dumlupınar University's Faculty of Engineering, Department of Electrical and Electronics Engineering. He holds a PhD in Electrical-Electronics Engineering and has been a Doctor Lecturer since 2023. His academic journey includes a BSc from Bilecik Şeyh Edebali University (2008-2012) and an MSc from Dumlupınar University (2013-2016). His research focuses on biomedical systems, EEG signal processing, and artificial intelligence applications in healthcare. Notable projects include investigating cognitive effects of aromatic scents using EEG and developing QCM-based biosensors for blood glucose monitoring. He teaches advanced machine learning, signal processing, and biomedical sensor courses. Key publications explore Alzheimer's detection via machine learning, EEG-based odor recognition, and glaucoma diagnosis using image analysis. He actively contributes to conferences and journals in biomedical engineering and AI. No awards or patents are listed. Advising records are unavailable, but his work emphasizes interdisciplinary collaboration in biomedical engineering and sensor technologies.

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.

Mehmet KOCATURKمشاهده پروفایل

استادیار

• Neuroprosthetics
• brain-machine interface
• computational neuroscience
• +۱ مورد دیگر

Mehmet KOCATURK is an Assistant Professor in the Department of Biomedical Engineering at Istanbul Medipol University, where he leads the Neuroprosthetics and Sensorimotor Research Group. His research specializes in neuroprosthetics, brain-machine interfaces, computational neuroscience, and biomedical instrumentation. Education Ph.D. in Biomedical Engineering, Bogazici University, 2015 M.Sc. in Biomedical Engineering, Bogazici University, 2007 B.Sc. in Electronics and Communication Engineering, Istanbul Technical University, 2004 Professional Experience Research Assistant, Bogazici University (2008-2015) Software Design Engineer, Nortel Networks Netas (2005-2008) Research Focus Dr. Kocaturk's work bridges neural engineering and clinical applications, with emphasis on developing implantable medical devices, neural signal processing algorithms, and motor skill learning mechanisms through neuroprosthetic systems. His projects often integrate computational modeling with experimental neuroscience. Teaching Activities Undergraduate Principles of Medical Device Design (2019-2020) Artificial Neural Networks (2018-2020) Biomedical Instrumentation (2017-2020) Microprocessors (2016-2020) Graduate Therapeutic Medical Devices (2019-2020) Neural Networks for Signal Processing (2018-2020) Fundamentals of Neuroengineering (2017) Computational Neuroscience (2016) Research Leadership He directs multiple funded projects including a TUBITAK-supported initiative to design implantable dopamine sensors and another investigating neural mechanisms of motor learning. He mentors undergraduate researchers in projects spanning ECG systems and closed-loop anesthesia devices.

Cemil ÖZمشاهده پروفایل

استاد

• Computer Science
• Artificial Intelligence
• Computer Vision
• +۹ مورد دیگر

Prof. Cemil ÖZ is a full-time Professor at Sakarya University's Faculty of Computer and Information Sciences, where he has held academic positions since 1998. He leads research in computer vision, artificial intelligence, and medical imaging applications, with ongoing projects in virtual simulation systems and machine learning diagnostics. Education PhD in Electrical and Electronics Engineering, Sakarya University (1995-1998), specializing in neural network-based robotic trajectory control Master's in Electronics and Computer Education, Marmara University (1989-1993), focusing on adaptive robotic control systems Bachelor's in Electronics and Communication Engineering, Yıldız University (1986-1989) Research Focus Prof. ÖZ's interdisciplinary research bridges computer science with healthcare, industrial applications, and security. Primary domains include: Medical AI : Machine learning for neuroimaging diagnostics (e.g., multiple sclerosis differentiation) Virtual Simulation : Industrial and educational systems (welding/driving simulators) Computer Vision : 3D reconstruction, object detection, and facial analysis Applied ML : Hybrid algorithms for predictive modeling in security and social media Publication Trends Recent articles (2019-2024) demonstrate strong emphasis on AI-driven healthcare solutions (38% of works), particularly neurological disorder diagnostics using MRI/spectroscopy data. Virtual training systems constitute 27% of output, focusing on industrial/educational simulations. Remaining publications advance computer vision techniques (3D reconstruction, object detection) and security applications (terrorism prediction, sentiment analysis). Methodologically, deep learning (YOLO, CNN) and sensor integration (Kinect) are predominant.

• Biomedical Signal Processing
• Deep Learning
• Machine Learning
• +۱۴ مورد دیگر

Ebru Sayılgan is an Assistant Professor at the Mechatronics Engineering Department of the Faculty of Engineering , Izmir University of Economics . She has served as Vice Dean (2024–present) and Coordinator for ERASMUS Exchange Programs (2021–present). Education : BSc/MSc in Mechanical Engineering (2013/2016) from Karadeniz Technical University , PhD in Biomedical Technologies (2020) from Izmir Katip Celebi University . Research Interests include biomedical signal processing , deep learning , machine learning , biomechatronics , and medical device design . She focuses on Brain-Computer Interfaces (BCIs) using EEG/ERP methods and rehabilitation robotics (e.g., low-cost wrist rehabilitation robots, hip implants). Her work integrates machine learning and signal processing for clinical applications. Journal articles cover topics like SSVEP analysis , ICA for EEG classification , and finite element analysis in implant design . Thesis Advisor to Hezzal Küçükselbes and Mehmet Furkan Çolak . She managed TUBITAK-funded projects on BCI-controlled wrist rehabilitation robots and hip implant prototyping . Editorial Work : Editor for Journal of Intelligent Systems with Applications (2022–2023).

• Biomedical Signal Processing
• Artificial Intelligence
• Control Systems
• +۷ مورد دیگر

Prof. Yalçın İşler is a distinguished academic at the Rafet Kayış Faculty of Engineering, Department of Electrical and Electronic Engineering, Alanya Alaaddin Keykubat University. He holds a PhD in Electrical-Electronic Engineering from Dokuz Eylül University and has served in various administrative roles including Department Head at Izmir Katip Çelebi University (2020-2025) and Bülent Ecevit University (2010-2012).

Mehmet İsmail GÜRSOYمشاهده پروفایل

استادیار

• Renewable Energy Systems
• Machine Learning Applications
• Signal Processing
• +۶ مورد دیگر

Mehmet İsmail GÜRSOY is an Assistant Professor at the Department of Electrical and Electronics Engineering, Faculty of Engineering, Adiyaman University. He holds a PhD from Kahramanmaraş Sütçü İmam University (2018), an MSc (2009), and a BSc from Firat University (2000). His academic career includes roles such as Deputy Director of Vocational School (2019–2023) and has extensive experience in engineering and project management prior to academia (2000–2004). Research interests focus on renewable energy systems, machine learning applications, signal processing in biomedical contexts (EEG/EMG), power quality analysis, and deep learning for disease detection. He has authored/co-authored 11 refereed journal articles and 13 conference proceedings, including works on photovoltaic efficiency modeling, biometric authentication via EMG signals, and wildfire susceptibility mapping using AI techniques. His book Versatile Multidisciplinary Engineering Research (2022) reflects his interdisciplinary approach. Notable recent contributions include machine learning-based PV power prediction, LSTM-driven PEM fuel cell lifetime estimation, and CNN-based skin disease classification. His work integrates environmental data analysis with neural networks and explores hydrogen production from brine. Administrative roles include leading technical departments and contributing to curriculum development in electrical engineering and computer technologies.

Fatma İnci Çilesizمشاهده پروفایل

استاد

• Biomedical Engineering
• Laser-Tissue Interactions
• Photothermal Applications
• +۵ مورد دیگر

Fatma İnci Çilesiz is a Professor in the Department of Electronics and Communication Engineering at Istanbul Technical University's College of Engineering. Her research focuses on the intersection of biomedical engineering, laser physics, and medical device development, with particular expertise in laser-tissue interactions and photothermal applications. Her research interests span multiple domains of biomedical engineering with emphasis on laser-tissue interactions , photothermal therapy , temperature feedback control systems , and non-invasive medical monitoring . Her work bridges engineering principles with clinical applications, particularly in developing precise control systems for laser-based medical procedures. The analysis of her publication record reveals consistent contributions to biomedical optics and laser medicine, with increasing focus on medical device development in recent years. Her research demonstrates strong interdisciplinary collaboration across engineering and medical domains, with applications ranging from tissue fusion techniques to non-invasive monitoring systems. As Principal Investigator, she has led significant research projects including the design of non-invasive blood sugar measurement devices and IoT-based fall detection systems for elderly care. Her work has resulted in 39 research outputs with an h-index of 12 and 515 citations, reflecting substantial academic impact in her field.

• Artificial Intelligence
• Human-Computer Interaction
• Robotics
• +۱۳ مورد دیگر

Assoc. Prof. Dr. Gökhan İnce is a faculty member at Istanbul Technical University (ITU) in the Department of Computer Engineering, under the Computer and Informatics unit. He holds a PhD from Tokyo Institute of Technology and has held research positions at Honda Research Institute in both Japan and Germany. His academic and research profile is deeply rooted in artificial intelligence, human-computer interaction, robotics, and smart wearable technologies. PhD, Tokyo Institute of Technology, 2008–2011 MSc, Technische Universität Darmstadt, 2004–2007 BSc, Istanbul Technical University, 1999–2004 His research spans a wide range of topics including speech recognition, emotion recognition, brain-computer interfaces, e-textiles, fog computing, and IoT-based rehabilitation systems . He actively develops intelligent systems for real-world applications such as telerehabilitation, scoliosis monitoring, and AI-driven enterprise solutions. The recent publication trends show a strong focus on textile-based wearable sensors, multimodal emotion recognition, fog computing frameworks, and AI integration in business processes . His work combines hardware innovation with advanced machine learning, particularly in adversarial and transfer learning paradigms. Scientific Awards and Recognitions: Üstün Başarı Ödülü, YÖK, 2024 En Başarılı Tez Ödülü, ITU, 2024 Best Paper Award, IARIA, 2017 and 2016 Best Presentation Award, Signal Processing Applications Conference, 2016 Best Paper and Best Student Paper, IEA-AIE Conference, 2010 BİDEB Postdoctoral Research Fellowship, 2014 He has led and supervised 30 research projects and over 125 research outputs , including numerous peer-reviewed articles and conference contributions. He serves as Principal Investigator (PI) on multiple ongoing projects in AI, smart textiles, and data integration. He is also a member of professional organizations including IEEE, ISCA, and ISAI, and contributes to open science initiatives. His work is conducted through interdisciplinary teams focusing on smart textiles, robotics, AI systems, and biomedical applications . These teams bridge engineering, computer science, and healthcare to develop next-generation assistive and intelligent technologies.

Murat Okatanمشاهده پروفایل

دانشیار

• Computational Neuroscience
• Neural Signal Processing
• Spike Detection
• +۷ مورد دیگر

Murat Okatan is an Associate Professor at Istanbul Technical University's Informatics Institute, Department of Computational Science and Engineering. He previously held academic positions at Cumhuriyet University in Electrical and Energy, Biomedical Engineering, and Mechatronics Engineering departments, and served in administrative roles including Department Chair, Head of Discipline, and Vice Dean. He received his PhD from Boston University and completed postdoctoral research at Ankara University and Boston University. PhD, Boston University MS, Syracuse University BS, Boğaziçi University (Dual Degree: Physics and Electrical-Electronics Engineering) His research lies at the intersection of computational neuroscience, neural signal processing, and biomedical engineering. He specializes in extracellular neural recordings, spike detection, brain-machine interfaces, and statistical modeling of neural data. His work includes developing automated thresholding techniques such as truncation thresholds for spike detection, analyzing subthreshold motor cortical activity, and modeling hippocampal place cells using Zernike polynomials. His recent publications focus on statistical significance testing in receptive field estimation and improving signal-to-noise ratio in neural recordings. His recent publications reveal a strong focus on statistical methods in neural data analysis, particularly in spike detection and receptive field modeling. He has developed and refined truncation threshold methods for more accurate action potential identification. His work bridges theoretical statistics with practical applications in brain-computer interfaces and neural decoding. He has also contributed to open-source tools, including Python code for parameter estimation in truncated distributions. YÖK Akademik Teşvik Ödeneği (2016, 2017) 2232 Postdoctoral Return Fellowship, TÜBİTAK (2011) Best Oral Presentation Award, 6th National Neuroscience Congress (2007) Best Oral Presentation Second Prize, 8th National Neuroscience Congress (2009) Presidential University Graduate Fellowship, Boston University (1997) High Honor Degree, Boğaziçi University (1995) Murat Okatan has supervised research projects funded by TÜBİTAK, the Turkish Higher Education Council, and international agencies including NIH, NSF, and ONR. He has served as a project executive and researcher in multiple scientific initiatives, including the Neuroscience and Neurotechnology Excellence Center (NÖROM). He has also acted as a guest editor for the Turkish Journal of Electrical Engineering and Computer Sciences . His lab focuses on developing computational tools for neural data analysis, with applications in brain-machine interfaces and neuroprosthetics. He is a member of several professional societies, including IEEE, IEEE Signal Processing Society, Society for Neuroscience (SFN), Brain Research Society (BAD), and Turkish Biophysics Society. His research group develops algorithms for real-time neural signal processing and contributes to national and international collaborative efforts in computational neuroscience.

• Signal Processing
• Image and Video Processing
• Artificial Intelligence
• +۷ مورد دیگر

Prof. MEHMET EMİN YÜKSEL is a Professor in the Department of Biomedical Engineering at the Faculty of Engineering, Erciyes University, where he has served since 2011. His academic career spans over three decades, progressing from Research Assistant (1991-1995) to Professor, with positions including Assistant Professor (2000-2005) and Associate Professor (2005-2011) at the same institution. He also held a research position at Imperial College of Science, Technology and Medicine in 1995. His educational background includes a Doctorate (1993-1996), Postgraduate (1991-1993), and Undergraduate (1986-1990) degrees in Electronics from Erciyes University and Istanbul Technical University. He has held significant administrative roles, serving as Head of the Biomedical Engineering Department (2011-2020) and Faculty Board Member. Prof. Yüksel's research focuses on Signal Processing, Image and Video Processing, Artificial Intelligence, and Biomedical Applications . His work bridges engineering principles with medical diagnostics, particularly in medical image analysis and biosignal processing. His publication record shows a progression from foundational work in fuzzy logic to advanced deep learning applications in medical diagnostics. His research themes consistently revolve around improving image and signal processing techniques with increasing medical applications. Recent work emphasizes deep learning for medical image classification, while earlier work focused on fuzzy logic and neural networks for general image processing. His notable contributions include: Development of type-2 fuzzy logic filters for image noise reduction Application of deep neural networks for medical diagnostics Hybrid optimization methods combining artificial bee colony and BFGS algorithms Techniques for biomedical signal analysis in obstetrics and dermatology Prof. Yüksel has supervised 20 thesis projects and participated in 15 funded research projects, resulting in 93 Web of Science publications with significant citation impact (H-index 14). He maintains active professional engagement as an IEEE member since 2005 and serves on the committee of the International Journal of Reasoning Based Intelligence.

• Machine Learning
• Computer Vision
• Brain-Computer Interfaces
• +۲ مورد دیگر

Huseyin Ozkan is an Associate Professor of Electronics Engineering at Sabancı University's Faculty of Engineering and Natural Sciences. His research spans machine learning, computer vision, signal processing, brain-computer interfaces, and visual neuroscience. He received his Ph.D. from Bilkent University (2015) and completed postdoctoral research at MIT (2017). Ozkan's scholarly output includes significant contributions to neural engineering and signal processing, with publications receiving recognition including the Alper Atalay Best Student Paper Award at IEEE SIU 2021. His research group currently advises Ph.D. candidate Soner Ozgun Pelvan and M.Sc. students Can Aksoy, Zobia Batool, and Berke Bakar Aksoy. Recent work demonstrates strong focus on medical AI applications and neural engineering innovations, particularly in Alzheimer's detection (2025), adaptive BCI systems (2023-2024), and video anomaly detection frameworks (2023). His laboratory utilizes computational neuroscience approaches to solve problems in healthcare technology and network systems.

• Brain-Computer Interface
• Biomedical Signal Processing
• Electroencephalography
• +۳ مورد دیگر

Aysa Jafari Farmand serves as a Lecturer at Istanbul Bilgi University, teaching core electrical engineering courses including Electrical and Electronic Circuits, Signals and Systems, and Numerical Methods. Her office is located in Santral Istanbul campus (E3-218). Her academic credentials feature: PhD in Electrical - Control Engineering, University of Tabriz (2017) MSc in Electrical - Control Engineering, University of Tabriz (2011) BSc in Electrical - Power Engineering, University of Tabriz (2008) Postdoctoral research at Istanbul Technical University (2021), Sabancı University (2020), and University of Tabriz (2019) Her research integrates biomedical engineering with artificial intelligence, specializing in Brain-Computer Interfaces and Electroencephalography signal analysis. She applies Machine Learning and Artificial Neural Networks to decode neural patterns, with emphasis on real-time biomedical applications and signal optimization techniques. No scientific awards were documented in the source material. Details regarding student supervision, research grants, or laboratory affiliations were not provided in the available information.

• Biomedical Signal Processing
• Machine Learning Applications
• Medical Imaging
• +۴ مورد دیگر

Prof. Dr. Ziya Telatar is a faculty member at Başkent University’s Biomedical Engineering Program. With over 30 years of experience, he focuses on biomedical signal/image processing, machine learning applications in medical diagnostics, and fuzzy logic systems. Education: PhD in Electrical-Electronics Engineering (1996), Ankara University Research Trends: Recent publications (2025-2014) emphasize deep learning for spinal curvature analysis , epilepsy focus detection via multimodal imaging , automated tumor segmentation , and RF fingerprinting . His work spans both diagnostic imaging and signal processing innovations. Projects: Active in applied research, including portable biosignal monitoring systems (2021) and phonocardiography-EKG integrated systems (2024). Teaching: Offers courses in Medical Electronics , Biomedical Instrumentation , and Signals & Systems .

• Biomedical Instrumentation
• Biomedical Engineering
• Signal Processing
• +۳ مورد دیگر

Dr. Onur Koçak is a Lecturer at Başkent University's Department of Computer Engineering. Holding a PhD in Electrical and Electronics Engineering from Ankara University (2014), he also earned a Master's (2008) and BSc (2005) in Biomedical Engineering from Başkent University. His work bridges biomedical sciences with advanced signal processing and machine learning. PhD: Electrical and Electronics Engineering, Ankara University (2014) MS: Biomedical Engineering, Başkent University (2008) BSc: Biomedical Engineering, Başkent University (2005) Koçak's research spans biomedical instrumentation, medical device design, and signal processing. His recent publications focus on deep learning applications for spinal deformity analysis, epilepsy detection via EEG, brain tumor segmentation in MRI, and antimicrobial nanoparticle synthesis. He has contributed to projects involving biosensors, wireless patient monitoring, and cardiovascular treatment systems. From 2021-2025, Koçak led or advised 16 Ar-Ge projects, including topics like Photocatalytic air purification systems EECP therapy monitoring Smart scoliosis detection Peripheral artery disease diagnostics His collaborations extend to companies such as Biyotek, İBÜTEM, and Marla Teknoloji, focusing on biomedical-electronic-HVAC integration.