جست‌وجوی استاد و پژوهشگر

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

Övgü Öztürk Ergün is an Assistant Professor in the Department of Software Engineering at Muğla Sıtkı Koçman University's Faculty of Engineering. She holds a Ph.D. from The University of Tokyo's Department of Frontier Informatics. Her research focuses on computer vision, machine learning, and AI applications in areas such as augmented reality, cultural heritage digitization, and human behavior analysis. She has led multiple TÜBİTAK-funded projects including 'Customer Tracking and Interest Measurement Technologies for Retail Sector Sales Performance Improvement' and '3D Modeling of Cultural Heritage Sites.' Her work spans academic publications, industry collaborations, and innovative teaching in software engineering and interactive multimedia systems. Education: Middle East Technical University, B.Sc. Electrical & Computer Engineering (2003) University of Tokyo, Ph.D. Frontier Informatics (2010) Research Interests: Her interdisciplinary research integrates computer vision with real-world applications, including privacy-preserving biometrics, smart environment monitoring, and cultural heritage preservation. She has pioneered ontology-based systems for Turkish cuisine and developed AR tools for virtual try-on. Her work emphasizes ethical AI and context-aware systems, with recent focus on food recognition using CLIP models and churn prediction in telecom sectors. Awards & Grants: TÜBİTAK Undergraduate Software Projects Competition (2013) Robot Days 3rd Place (2013) Completed TÜBİTAK 1001/1505 projects totaling over 500k USD funding Advising & Labs: Supervised BENGÜ-ÖZTÜRK's thesis on food image recognition. Active in the University's Research & Application Centers for Computer Vision and AR/VR development. Collaborates with ISTKA and Ministry of Development on cultural heritage digitization projects.

• Computer Vision
• Deep Learning
• 3D Point Cloud Processing
• +۷ مورد دیگر

Yusuf Hüseyin Şahin is an Assistant Professor in the Department of Computer Engineering at Istanbul Technical University, Faculty of Computer and Informatics. He earned all his academic degrees—B.Sc., M.Sc., and Ph.D.—from the same institution in Computer Engineering. His research lies at the intersection of computer vision, deep learning, and 3D data processing, with applications in medical imaging, architectural heritage, and drone-based vision systems. B.Sc., M.Sc., Ph.D. in Computer Engineering, Istanbul Technical University His primary research interests include 3D point cloud processing, deep learning, image segmentation, adversarial attacks, and medical image analysis. He has published extensively on these topics, particularly focusing on point cloud registration, segmentation, and classification using neural networks. His recent work explores uncertainty modeling, active learning, and generative models for both synthetic data creation and real-world applications. The trend in his publications from 2017 to 2024 shows a clear progression from foundational work in CNN-based 3D classification and cerebral vessel analysis to advanced topics such as dynamic graph networks, conformal prediction, and heritage digitization. His work bridges theoretical machine learning with practical applications in healthcare and cultural preservation. He is currently leading a research project titled "Konformal Tahmin ile Sıcaklık Tahmin Modellerinde Doğruluğun Arttırılması" (Improving Temperature Prediction Accuracy Using Conformal Forecasting), funded under the SRP program from 2025 to 2026. This indicates an expanding interest in predictive modeling and uncertainty quantification. While no scientific awards are listed in the provided texts, his h-index of 5 and 293 citations on Scopus reflect an active and growing research profile. Dr. Şahin teaches undergraduate courses such as Data Structures (BLG 223E) and Object-Oriented Programming (BLG 252E). He has no listed advisees or thesis supervision records. He is part of a collaborative research network involving Gozde Unal and other researchers in medical and architectural computer vision. His lab activities appear to focus on deep learning for 3D data, with emphasis on robustness, efficiency, and real-world deployment.

• Artificial Intelligence
• Medical Image Computing
• Computer Vision
• +۹ مورد دیگر

Gözde Ünal is a Professor at Istanbul Technical University (ITU) in the Department of Artificial Intelligence and Data Engineering, under the School of Computer and Informatics. She has held leadership roles including Deputy Dean and former Director of the Application and Research Center. She previously served as Associate and Assistant Professor at Sabancı University and taught at Georgia Institute of Technology. Education: Ph.D., Electrical and Computer Engineering, North Carolina State University (1998–2002) M.Sc., Electrical and Electronics Engineering, Bilkent University (1996–1998) B.Sc., Electrical and Electronics Engineering, Middle East Technical University (1992–1996) Her research focuses on medical image computing, computer vision, deep learning, augmented and virtual reality, and AI for healthcare . She specializes in image segmentation, point cloud processing, and surgical scene analysis. Her work bridges computer science with biomedical applications, particularly in MRI, ultrasound, and dermoscopic imaging. She leads interdisciplinary research in both medical and cultural heritage domains. The recent publications (2021–2025) highlight a strong trend in deep learning for medical image segmentation , adversarial attacks on point clouds , AI in surgical skill assessment , and cultural heritage digitization using 3D point clouds . Her work increasingly employs transformers, self-supervised learning, and multimodal fusion, with a focus on robustness and real-world deployment. Scientific Awards: Fakülte Yılın Seçkin Eğitimcisi Ödülü, ITU, 2018 Marie Curie Alumni Association Career Award, 2017 GENÇ BİLİM KADINI, L’Oréal Turkey, 2010 GEBİP, Turkish Academy of Sciences, 2010 She has supervised numerous students and research projects, securing grants from EU and TTO (Technology Transfer Office). Her projects include AI-based optimization in banking, AI for assisted reproduction, deep learning for quality control, and normalization of multimodal brain networks. She leads a dynamic research group working on cutting-edge AI applications. She is actively involved in professional societies: President of the Marie Curie Alumni Association Turkish Chapter, member of MICCAI, IEEE, and TMRD. Her lab focuses on AI-driven solutions in medical imaging and heritage preservation, with strong international collaborations, particularly in Europe.

• Data Mining
• Web Mining
• Recommendation Systems
• +۱۲ مورد دیگر

Prof. Şule Öğüdücü is a full-time Professor in the Department of Artificial Intelligence and Data Engineering at Istanbul Technical University (ITU), Faculty of Computer and Informatics. She has been serving as the Department Chair since 2019 and has held various administrative roles including Associate Dean (2012–2015) and Faculty Board Member. Her academic journey began with a B.Sc. in Electronics and Communication Engineering from ITU, followed by an M.Sc. in Biomedical Engineering from Boğaziçi University, and a Ph.D. in Computer Engineering from ITU. During her doctoral studies, she was a Research Associate at the University of Waterloo’s Database Laboratory (2001–2003), supported by NSERC and Tinçel Foundation grants. B.Sc., Electronics and Communication Engineering, Istanbul Technical University, 1987–1991 M.Sc., Biomedical Engineering, Boğaziçi University, 1991–1995 Ph.D., Computer Engineering, Istanbul Technical University, 1999–2003 Her research is centered on data mining, web mining, recommendation systems, social network analysis, big data, community detection, and forecasting applications in finance and demand. She is recognized internationally, ranking 27th in Google Scholar in recommendation systems and 1st in Turkey. Her recent work emphasizes AI-driven solutions in network security, explainable AI, and industrial applications such as oil industry flash point prediction and e-commerce demand forecasting. The analysis of her recent publications (2022–2025) reveals a strong trend toward integrating deep learning and graph neural networks (GNNs) into recommendation and network anomaly detection systems. There is a growing focus on explainability (XAI), semi-supervised learning, and real-world applications in critical infrastructure (e.g., backbone networks, traffic, oil industry). Her work bridges theoretical AI advancements with practical implementations in cybersecurity, smart cities, and industrial automation. Her scientific honors include: Siemens Excellence Award, 2004 TÜBİTAK Threshold Award, 2024 Prof. Öğüdücü has led numerous research projects, including TÜBİTAK-funded initiatives and ITU Research Fund grants. She is the Principal Investigator (PI) of multiple ongoing projects such as 'Explainable AI Methods for Earthquake Prediction,' 'AI-Based Demand Forecasting for the Fashion Industry,' and 'Black Hole Anomaly Detection in Backbone Networks.' She also engages in sectoral collaborations with companies like Ericsson, KariyerNet, and IDEA Technology, providing academic consultancy in dynamic pricing, financial analysis, and real-time system scaling. She has supervised 33 academic works, reflecting her active role in mentoring students. Additionally, she founded the IEEE Turkey WIE Affinity Group in 2010, contributing significantly to the promotion of women in engineering. She leads research teams working on AI and data engineering applications, particularly in network security, recommendation systems, and industrial AI. Her labs focus on developing benchmark datasets (e.g., IBB Traffic Graph Data), predictive models, and explainable AI frameworks. Current teams are engaged in projects funded by TÜBİTAK and industry partners, emphasizing real-time, scalable, and interpretable AI solutions.

Müjdat Çetinمشاهده پروفایل

استاد

• Signal Processing
• Image Processing
• Data Science
• +۱۲ مورد دیگر

Müjdat Çetin is a Professor of Electrical and Computer Engineering and serves as the Robin and Tim Wentworth Director of the Goergen Institute for Data Science and Director of the New York State Center of Excellence in Data Science at the University of Rochester. He previously held faculty positions at Sabancı University and was a Research Scientist at MIT, with visiting roles at Boston University, Northeastern University, and MIT. Education: PhD in Electrical Engineering, Boston University, 2001 MS in Electrical Engineering, University of Salford, 1995 BS in Electrical Engineering, Boğaziçi University, 1993 His research lies at the intersection of signal processing, machine learning, and data science, with applications in biomedical imaging, radar, and brain-computer interfaces. He develops probabilistic and deep learning models for robust information extraction from noisy and complex data. His work emphasizes computational imaging, sparse representations, and multimodal data fusion. The recent publications reflect a strong trend toward integrating Bayesian methods and deep learning in imaging sciences, particularly in medical image reconstruction, neuroimaging analysis, and radar systems. His group actively explores transformer architectures, federated learning, and model-based deep learning for solving inverse problems in imaging. Scientific Awards and Honors: IEEE Fellow IEEE Signal Processing Society Best Paper Award IET Radar, Sonar and Navigation Premium Award Elsevier Signal Processing Best Paper Award Turkish Academy of Sciences Distinguished Young Scientist Award (GEBİP) ODTÜ Mustafa Parlar Foundation Research Incentive Award TÜBİTAK Career Award Boston University Best Engineering Research Award Professor Cetin has advised numerous PhD and Master’s students and led significant research grants in data science and imaging. He has served as a Senior Area Editor for IEEE Transactions on Image Processing and IEEE Transactions on Computational Imaging, and held editorial roles in several top journals. He has chaired major conferences including ICASSP, ICIP, and IVMSP workshops. He leads a multidisciplinary research group focused on data science and imaging, collaborating with neuroscientists and medical researchers. The team develops novel algorithms for brain-computer interfaces, medical image analysis, and remote sensing systems, often integrating machine learning with physical models of data acquisition.

Ali Taylan Cemgilمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Machine Learning
• Time Series Analysis
• +۴ مورد دیگر

Ali Taylan Cemgil is an Associate Professor at Bogazici University's Department of Computer Engineering, College of Engineering. His research focuses on Bayesian statistics, machine learning, and audio/music processing within the Perceptual Intelligence Laboratory (PILAB). PhD in Computer Science from Radboud University Nijmegen (2004) Postdoctoral research at University of Amsterdam (Intelligent Autonomous Systems Lab) and University of Cambridge (Signal Processing and Communications Lab) Research Interests: Bayesian modeling and time series analysis Audio signal processing and source separation Human-AI collaboration frameworks Probabilistic methods in AI reliability and fairness Scientific Contributions: Recent work explores conformal prediction for model calibration, adversarial robustness in deep learning, and fairness-aware medical AI systems. His research spans theoretical foundations in Bayesian statistics and practical applications in indoor localization and capsule robotics. Academic Service: Current faculty member with extensive publications in AI/ML, signal processing, and probabilistic modeling.

• Financial Mathematics
• Econometrics
• Risk Management
• +۵ مورد دیگر

Ayşegül İşcanoğlu ÇEKİÇ is an Associate Professor in the Department of Econometrics at Trakya University's Faculty of Administrative Sciences. She holds dual doctoral degrees from Middle East Technical University (2011) and Technische Universität Kaiserslautern (2010), both in Financial Mathematics. Her academic career spans roles as a Research Assistant at Middle East Technical University (2004-2011), Assistant Professor at Selçuk University (2011-2014), and Associate Professor at Trakya University since 2014. Education: B.Sc. in Statistics, Middle East Technical University (2003) M.Sc. in Financial Mathematics, Middle East Technical University (2005) Ph.D. in Financial Mathematics, Middle East Technical University (2011) Second Ph.D. in Financial Mathematics, Technische Universität Kaiserslautern (2010) Her research focuses on financial mathematics, econometrics, and risk management. Key areas include portfolio insurance strategies, credit scoring, debt obligations, and stochastic modeling in financial markets. She has extensively studied Constant Proportion Portfolio Insurance (CPPI), risk-return dynamics in emerging markets, and generalized additive models in finance. Her work frequently addresses Turkey's financial sector through empirical analyses of BIST indices, pension funds, and environmental disclosures. Her publications demonstrate methodological rigor in computational finance, with applications to Turkish and international markets. She explores cross-correlations, multifractal analysis, and heavy-tailed distributions for risk assessment, alongside shrinkage estimators and Bayesian approaches for portfolio optimization. Scientific Awards: ULAKBIM UBYT (International Scientific Publications Incentive) - TÜBITAK (2014) ÇEKİÇ has served on editorial boards for the Social Sciences Research Journal and International Journal of Mathematics and Statistics . She has refereed for prestigious journals including Annals of Operations Research and Mathematical Problems in Engineering . Active in international conferences, she organized the 16th International Symposium on Econometrics (2015) and held leadership roles in sessions at European Conference on Operational Research.

• Robotics
• Mechatronic System Design
• Computer Aided Design and Manufacturing
• +۱ مورد دیگر

Cüneyd DEMİR is a full-time Lecturer at Ahi Evran University's Mucur Vocational School, Department of Computer Technologies. He has held this position since 2019 and was promoted to Doktor Öğretim Görevlisi (Lecturer with PhD) in 2024. His administrative roles include Department Head (2021-2023), Vice Director of the Vocational School (2021-present), and membership in university committees (Quality Board, Management Council). He holds a PhD in Industrial Design Engineering from Gazi University (2020-2024), an MSc in Defense Technologies from Kırıkkale University (2015-2017), an MSc in Occupational Health and Safety from Aksaray University (2019), and a BSc in Mechatronic Engineering from Gaziosmanpaşa University (2011-2015). His research focuses on Robotics , Mechatronic System Design , Computer-Aided Design/Manufacturing (CAD/CAM) , and Artificial Intelligence , with emphasis on unmanned ground vehicles (UGVs), defense systems, renewable energy integration, and AI-driven design methodologies. Publications primarily explore UGV design using machine learning, solar-powered autonomous systems, and material science for robotics. Recent works demonstrate strong trends in AI/neural network applications for defense technology and systematic design processes. He contributed to the TÜBA/TÜBİTAK-supported project: "Güneş Enerjisi İle Uçuş Süresi Artırılmış Otonom Model Uçak" (Solar-Powered Autonomous Model Aircraft with Extended Flight Duration, 2014-2015) as a researcher.

Aissa Houdjedjمشاهده پروفایل

پژوهشگر

• Machine Learning
• Artificial Intelligence
• Bioinformatics Algorithms
• +۲ مورد دیگر

Aissa Houdjedj is a Researcher in the Department of Computer Engineering at Antalya Bilim University (Antalya, Turkey) and a Ph.D. candidate in Computer Engineering at Akdeniz University. His roles include contributing to TÜBİTAK research projects in bioinformatics. Doctorate: Akdeniz University Master's: Antalya Bilim Üniversitesi Bachelor's: Antalya Bilim Üniversitesi His research focuses on: Bioinformatics Algorithms Computational Analysis of Biological Networks Design Machine Learning Artificial Intelligence Graph Algorithms Engineering

ENİS ARSLANمشاهده پروفایل

استادیار

• Natural Language Processing
• Image Processing
• Artificial Intelligence
• +۴ مورد دیگر

ENİS ARSLAN is an Assistant Professor at the Department of Computer Engineering, Faculty of Engineering, Çanakkale Onsekiz Mart University. His research spans Natural Language Processing, Image Processing, Artificial Intelligence, and Remote Sensing. Ph.D. in Computer Engineering from Çukurova University (2020) Master's in Computer and Information Sciences from Doğuş University (2013) Master's in Business Administration from Beykent University (2009) B.Sc. in Computer Engineering from Yıldız Technical University (2001) His work focuses on UAV-based 3D documentation of cultural heritage, flood mapping with machine learning, and Turkish morphological disambiguation. He has contributed to journals like ACM Transactions on Asian and Low-Resource Language Information Processing and Environmental Monitoring and Assessment . His projects include Tübitak-funded initiatives on smart tracking systems and UAV applications in geospatial analysis. He has taught courses in software development and computational linguistics.

Tuğrul TAŞCIمشاهده پروفایل

استادیار

• Artificial Intelligence
• Natural Language Processing
• Computer Vision
• +۳ مورد دیگر

Tuğrul TAŞCI serves as an Assistant Professor in the Department of Information Systems Engineering at Sakarya University's Faculty of Computer and Information Sciences, where he has maintained continuous academic service since 2001. His career progression includes Research Assistant positions across multiple university units before advancing to his current faculty role in 2016. His academic credentials include: Doctorate in Computer and Information Engineering (2014) from Sakarya University Institute of Science, thesis: Real-Time Motion Tracking with Particle Filtering Based on Data Fusing Master's degree in Computer and Information Engineering (2004) with thesis: Design of an Integrated Web-Based Distance Education System Bachelor's degree in Computer Engineering (2001) with thesis: Course Scheduling with Genetic Algorithms Dr. TAŞCI's research centers on Artificial Intelligence applications, particularly Natural Language Processing for Arabic text and Computer Vision . His work integrates particle filtering , data fusion , and optimization algorithms (e.g., Artificial Bee Colony, Firefly) to solve problems in text summarization, motion tracking, and image processing. Recent publications demonstrate expansion into deep learning for industrial defect detection and time series analysis. Analysis of his 2019-2024 publications reveals three dominant research trajectories: (1) Arabic NLP with focus on extractive summarization using PageRank and word embeddings, (2) Computer vision systems for motion tracking and text detection leveraging particle filters and curvature features, and (3) Hybrid optimization techniques applied to diverse domains from emergency management to customer churn prediction. Current academic advising activities and research grant details are not publicly documented in available sources. Similarly, no institutional laboratories or research teams are explicitly associated with his profile in the provided materials.

İLKAY SİBEL KERVANCIمشاهده پروفایل

استادیار

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

İLKAY SİBEL KERVANCI serves as an Assistant Professor in the Department of Computer Engineering at Gaziantep University's Faculty of Engineering. Her academic career spans teaching and research in artificial intelligence, machine learning, and data mining with practical applications across finance and bioinformatics sectors. PhD in Computer Engineering, Çukurova University (2023) MSc in Informatics, Kahramanmaraş Sütçü İmam University (2017) BSc in Computer Engineering, Kocaeli University (2004) Her research centers on machine learning applications for cryptocurrency price forecasting using LSTM and GRU networks, neutrosophic logic implementations in regression problems, and drug-target interaction prediction. Recent work demonstrates expertise in hyperparameter optimization, time series analysis, and handling imbalanced datasets through hybrid feature reduction techniques. She actively contributes to advancing neural network architectures for financial and biomedical challenges. Publication trends reveal consistent focus on Bitcoin price prediction (6 publications since 2017), expanding into drug-target interaction modeling and neutrosophic applications. Her work bridges theoretical machine learning with industrial applications in cement manufacturing, stock markets, and pharmaceutical research through recurrent neural networks and optimization frameworks. Dr. Kervanci teaches graduate courses including Introduction to Artificial Intelligence Methods and Introduction to Data Mining Methods, alongside undergraduate courses such as Artificial Intelligence in Engineering and Discrete Mathematics, demonstrating commitment to computational education across academic levels.

Mehmet Can Yavuzمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Human-Computer Interaction
• +۳ مورد دیگر

Mehmet Can Yavuz is an Assistant Professor at Işık University's Faculty of Engineering and Natural Sciences, Department of Computer Engineering. As Principal Investigator of the Multimedia Lab, he bridges machine learning with artistic expression through projects like Arky Multimedia, ConvergedMachine, and Duyukoru. His research spans biomedical imaging, human-computer interaction, and cross-modal analysis of multimedia storytelling. 2019-2023: PhD in Computer Science & Engineering, Sabancı University 2010-2016: MS in Physics, Boğaziçi University 2006-2010: BS in Physics, Işık University 2004-2010: BS in Electrical-Electronics Engineering, Işık University Current research explores: Advanced machine learning architectures (Variational Contrastive Learning, Cross-D Convolution) Biomedical imaging applications for disease detection Computational analysis of dramatic/literary works through graph theory and sentiment analysis AI-driven threat detection systems using sensor fusion Creative technology intersections in multimedia production His lab develops frameworks for: Noisy data processing in semi-supervised learning Cross-dimensional knowledge transfer Ensemble approaches in 2D/3D medical imaging Temporal-sentiment analysis of urban events Document embedding-based character analysis Projects include: ARKY MULTIMEDIA - Combining creative exploration with ML DUYUKORU - Machine learning-enhanced sensor threat detection CONVERGEDMACHINE - Multimodal ML research repository He oversees the Işık University Multimedia Lab , which integrates medical image computing with animation production, pushing boundaries in both scientific and artistic domains.

• Biomedical Engineering
• Bioelectronics
• Artificial Intelligence
• +۳ مورد دیگر

Rüştü Murat Demirer serves as an Assistant Professor in the Department of Electrical and Electronics Engineering at Işık University's Faculty of Engineering and Natural Sciences. His academic career spans decades with active teaching responsibilities including Biomedical Engineering courses such as Clinical Care Informatics, Biosignal Processing, and Medical Imaging since at least 2012 across multiple institutions including Işık University and Bahçeşehir University. His educational foundation includes: PhD in Biomedical Engineering from Boğaziçi University (1983-2002) MS in Energy from Istanbul Technical University (1980-1982) BS in Electronics and Communications Engineering from Kocaeli University (1976-1980) Dr. Demirer's research integrates Biomedical Engineering with cutting-edge computational neuroscience, focusing on Bioelectronics, Artificial Intelligence applications, and Neuroscience. He pioneers methodologies for analyzing brain dynamics through EEG/ECoG signal processing, entropy-based biomarker development, and machine learning algorithms for neurological and psychiatric conditions. His work bridges theoretical neuroscience with clinical applications in epilepsy, bipolar disorder, and brain-computer interfaces. Analysis of his publication trends reveals strong interdisciplinary convergence between neuroscience, biomedical engineering, and artificial intelligence. Key methodological themes include Hilbert transform applications, nonlinear dynamics in brain signals, entropy quantification for psychiatric diagnostics, and hybrid machine learning approaches for medical signal classification. This research trajectory demonstrates consistent innovation in translating complex brain signal analysis into clinically relevant diagnostic tools. Dr. Demirer has actively mentored 11 graduate students (10 Master's and 1 PhD) between 2013-2025. His advisees' research spans diverse applications including: Machine learning for cybersecurity threat detection Cryptocurrency market analysis using predictive modeling EEG/eye-tracking fusion for cognitive decision studies Medical diagnostics through convolutional neural networks Natural language processing for offensive language detection He maintains professional engagement as a member of the Chamber of Electrical Engineers (Elektrik Mühendisleri Odası) while teaching specialized courses across biomedical engineering, cybersecurity, and data science domains.

• Medical Biology
• Machine Learning Applications in Medicine
• Medical Imaging Analysis
• +۳ مورد دیگر

Muhammed Kamil Turan is an Associate Professor at Karabük University, Faculty of Medicine, Department of Basic Medical Sciences Medical Biology . He has held leadership roles including Deputy Dean (2013-2020) and Program Coordinator (2012-present). PhD in Medical Biology (2011) from Ondokuz Mayıs University Master's in Computer Engineering (2004) from Karabük University Medical Degree (2000) from Ondokuz Mayıs University His research focuses on machine learning applications in medical biology , with particular emphasis on sex/gender determination from CT images , DNA damage analysis , and automated biomedical imaging systems . He has developed innovative solutions for peripheral blood smear analysis, retinal microaneurysm detection, and low-cost medical device design. Recent research trends show strong integration of artificial intelligence in diagnostic imaging (cranial/pelvic CT analysis), bioinformatics (protein structure prediction), and biomedical instrumentation (automated microscopes, DNA electrophoresis systems). His 2025 work explores fractal network graphs in medical contexts. He serves as principal advisor for 11 graduate students and collaborates extensively with researchers across Turkey. His projects often involve TÜBİTAK/TÜBA funding , with recent work on cold plasma applications in wound healing and gastric ulcer treatment.