جستجوی استاد راهنمای دانشگاهی

Osman Gökalpمشاهده پروفایل

استادیار

• Evolutionary Computation
• Metaheuristic Algorithms
• Machine Learning
• +۵ مورد دیگر

Osman Gökalp is an Assistant Professor in the Department of Computer Engineering at Izmir Institute of Technology (IZTECH). He holds B.Sc., M.Sc., and Ph.D. degrees from Ege University's Computer Engineering department (2010, 2012, 2018). Previously, he served as a research assistant at Yaşar University (2011-2012) and Ege University (2014-2021), followed by an assistant professor position at Izmir Katip Çelebi University (2021-2024). His research focuses on evolutionary computation, metaheuristic algorithms, machine learning, and optimization problems with applications in medical imaging, cloud computing, and wireless sensor networks. Education: B.Sc. Computer Engineering, Ege University (2010) M.Sc. Computer Engineering, Ege University (2012) Ph.D. Computer Engineering, Ege University (2018) Research Interests: His work spans algorithm design for optimization problems, including metaheuristics like Greedy Construction and Differential Evolution. Notable areas include DNA motif discovery, task scheduling in cloud computing, and Alzheimer's disease detection via medical imaging. Projects: Contributions include repositories like 'WSN_Coverage_And_Connectivity' (Differential Evolution for sensor networks), 'CloudTaskSchedulingOptimization' (metaheuristics for cloud task scheduling), and 'MotifDiscovery' (DNA sequence analysis using GRASP metaheuristic). Labs/Teams: Active in IZTECH's Computer Engineering research groups, focusing on computational intelligence and algorithm development for real-world applications.

Alper Kürşat Uysalمشاهده پروفایل

دانشیار

• Text Classification
• Feature Selection
• Pattern Recognition
• +۲ مورد دیگر

Alper Kürşat Uysal is an Associate Professor of Computer Engineering at Alanya Alaaddin Keykubat University. His research develops advanced text classification algorithms and feature selection methods, with applications ranging from spam detection to educational analytics. Research foci: Novel feature selection metrics for text data Imbalanced text classification techniques Short text analysis methodologies Multilingual spam detection systems Affective computing in educational contexts Uysal's publications introduce innovative approaches to feature selection, including the Extensive Feature Selector method and specialized metrics for short text datasets. His work addresses practical challenges in Turkish-language social media content moderation and develops automatic classification systems for educational applications. Recent research examines how feature selection impacts classifier performance on imbalanced text corpora.

Hakan Emekciمشاهده پروفایل

استادیار

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

Hakan Emekci is an Assistant Professor at TED University's Graduate School of Applied Data Science. He teaches courses such as Statistical Learning, Data Analytics, and Information Retrieval in the Applied Data Science Master Program. His research focuses on AI applications in healthcare, education technology, econometrics, and data-driven methodologies. Emekci has published works on specialized language models for medical contexts, RAG systems optimization, and the integration of AI in educational frameworks. His recent studies emphasize domain-specific AI solutions and their practical implementations across diverse sectors. Emekci's academic contributions include analyzing central bank policies' impact on financial markets and developing computational frameworks for data mining. He actively teaches advanced courses at both undergraduate and graduate levels, including Data Structures and Algorithms. His work bridges theoretical data science concepts with real-world applications in healthcare, education, and finance. Notable research areas include: Healthcare informatics & AI-driven diagnostic systems Educational technology innovations Machine learning model optimization Financial econometric analysis Domain-specific large language models His publications from 2025 highlight advancements in multi-agent medical frameworks and comparative studies of retrieval-augmented generation techniques. Earlier work (2017-2018) established foundational research in econometric policy analysis and R-based data mining methodologies.

• Natural Language Processing (NLP)
• Computational Linguistics
• Artificial Intelligence
• +۱۱ مورد دیگر

Prof. Dr. Gülşen Cebiroğlu Eryiğit is a Professor at Istanbul Technical University (ITU) in the Department of Artificial Intelligence and Data Engineering under the Faculty of Computer and Informatics. As the founder and director of the ITU Natural Language Processing Group , she leads Turkey's foremost research team in Turkish-language NLP. Her work has achieved global impact through the first software export from ITU in NLP, with 6,000+ Google Scholar citations as of 2023. Education: MSc & PhD, Computer Engineering, ITU (2002-2007) BSc, Computer Engineering, Marmara University (1995-2000) High School: Saint-Michel (French curriculum) Her research focuses on natural language processing for morphologically complex languages like Turkish, with specializations in: Dependency parsing and treebank development Multilingual transfer learning systems Abstract meaning representation Language technology infrastructure Coreference resolution in agglutinative languages Gamification of linguistic education Recent publications show active research trends in: Transforming financial document analysis with BiLSTM-CRF architectures Advancing Turkish language models through ViBERTgrid implementations Developing multilingual frameworks under EU COST Actions Creating semantic search engines for job matching platforms Optimizing automotive demand management through dialogue systems Building large language models (LLMs) for Turkish Scientific recognition includes: Siemens Excellence Award (2007) for doctoral research Above Threshold Award (TÜBİTAK, 2015) EU 7th Framework Certificate of Appreciation (2012) ITU Thank You Plaque (2017) Current leadership roles: Senior Action Editor, ACL RR Director, ITU TÖMER (Turkish Language Teaching Center) Coordinator, ITU NLP Group Co-Chair, EU UniDive COST Action WG3 Principal Investigator on 3 active projects

Faik Boray Tekمشاهده پروفایل

دانشیار

• Medical Image Analysis
• Deep Learning
• Artificial Neural Networks
• +۷ مورد دیگر

Assoc. Prof. Dr. Faik Boray Tek is a faculty member in the Department of Artificial Intelligence and Data Engineering at Istanbul Technical University (ITU), Faculty of Computer and Informatics. He joined ITU in August 2022 after serving as an Assistant Professor in Computer Engineering at Işık University from 2009 to 2022. His research bridges artificial intelligence and healthcare, with a strong focus on medical image analysis. Education: PhD in Medical Image Analysis, University of Westminster, UK (2007) MSc in Electrical and Electronics Engineering, Middle East Technical University (2003) BSc in Electronic Engineering, Başkent University (2000) His research interests include deep learning, artificial neural networks, medical image analysis, malaria diagnosis, and retinal disease detection . He leverages advanced AI models for diagnosing Alzheimer's disease and diabetic retinopathy using optical coherence tomography (OCT) and OCT angiography (OCTA). His work also extends to Text-to-SQL systems and neuron modeling. Recent publications (2023–2025) demonstrate a strong trend in medical AI , particularly in ophthalmology and neurodegenerative disease diagnosis. He employs foundational models, segmentation techniques, and hybrid architectures to analyze OCT and OCTA scans. Additional work focuses on time-series forecasting via enhanced recurrent units and systematic reviews of AI in healthcare. His publications appear in IEEE Access, Pattern Recognition, and UBMK proceedings. Scientific Awards: Above Threshold Award, TÜBİTAK (2024) Dr. Tek leads active research projects funded by TÜBİTAK and ITU-BAP, including "Optical Coherence Tomography (and Angiography) Scans and Deep Learning-Based Alzheimer's Disease Diagnosis" and "Two-Dimensional Adaptive Local Connected Neuron Model" . He has advised multiple theses and supervised research outputs, indicating active mentorship. His collaborations include Heidelberg Collaboratory for Image Processing (2015). He is affiliated with research labs and teams at ITU focused on AI and data engineering, contributing to both academic and applied innovations in medical diagnostics and neural architectures.

Tolga Kayaمشاهده پروفایل

استاد

• Multi-Criteria Decision Making
• Data Analytics
• Business Analytics
• +۶ مورد دیگر

Prof. Dr. Tolga Kaya is a faculty member at Istanbul Technical University , Department of Management Engineering. His academic journey includes roles as Professor (2021-present), Associate Professor (2016-2021), Doctor Lecturer (2011-2016), Lecturer (2009-2011), and Research Assistant (2005-2009). Education : PhD in Management Engineering (2003), MSc in Management Engineering (1998), BSc in Industrial Engineering (1993) His research spans Multi-Criteria Decision Making , Data Analytics , and Fuzzy Decision Methods , with recent publications focusing on machine learning applications in retail, e-commerce engagement, and Industry 4.0. He supervises graduate theses on topics like anomaly detection, customer review analysis, and digital advertising optimization. Key projects include DNext SFA-CM (cloud-based sales force automation), RET Site (engineering research for rural development), and studies on consumer loan evaluation and real estate analysis.

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.

• Natural Language Processing
• Generative AI
• Social Computing
• +۵ مورد دیگر

Cagri Toraman is an Assistant Professor at Middle East Technical University's Computer Engineering Department, leading the Applied NLP Group. He holds a PhD in Computer Science from Bilkent University (2017) and has industry experience as Software Engineer (Havelsan 2017-2018), Postdoctoral Research Scientist (University of Central Florida 2018-2019), and NLP Team Lead (Aselsan 2020-2023). PhD in Computer Science (Bilkent University, 2017) Postdoctoral Research Scientist (University of Central Florida, 2018-2019) NLP Team Lead (Aselsan, 2020-2023) His research focuses on Natural Language Processing , Generative AI , and Social Computing , with particular emphasis on low-resource language adaptation, hate speech detection, multimodal analysis, and misinformation identification. Recent work examines dataset quality evaluation, transformer optimization with tweet-specific elements, and pejorative language disambiguation for misogyny detection. Notable achievements include receiving the Google Gemma Academic Research Award 2024 . He teaches core courses in Data Structures and Applied Natural Language Processing , emphasizing both theoretical foundations and practical implementation. Research trends show expertise across: Multilingual dataset creation (MiDe22, JL-Hate) Transformer-based hate speech detection Generative AI adaptation for Turkish Ensemble learning in socio-political event extraction Contextual word embeddings analysis Conversational system robustness

Deniz Yuretمشاهده پروفایل

استاد

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Deniz Yuret is a Professor in the Department of Computer Engineering at Koç University , Istanbul, and the founding director of the KUIS AI Center . Previously, he spent 12 years at the MIT AI Lab and co-founded Inquira, Inc. His research focuses on Natural Language Processing and Machine Learning , with significant contributions in dependency parsing , language modeling , grounded language learning , and character-level NLP . He has pioneered frameworks like Knet , a deep learning library in Julia, and AutoGrad.jl for automatic differentiation. Deniz's academic work spans neural architectures for language-robot interaction, transfer learning in low-resource NMT, and context embeddings for grammatical category acquisition. His recent publications emphasize transformer models , multimodal systems , and efficient language modeling . He has supervised multiple graduate students, including Emre Can Açıkgöz (PhD, UIUC), Onur Kuru (M.S. 2016), Saman Zia (M.S. 2016), and Osman Baskaya (M.S. 2015). His projects include the TUBITAK 1001 (2016-2018) and ReGROUND (2015-2018) in collaboration with international institutions.

• Reinforcement Learning
• Natural Language Processing
• Machine Learning
• +۳ مورد دیگر

Dr. Eren Ulu is a Lecturer in the Department of Computer Engineering at TED University, where he has taught since 2014 (initially as Research Assistant, promoted to Lecturer in 2019). His academic journey includes a Bachelor's degree earned with top honors, followed by Master's and PhD studies at Hacettepe University's Computer Graphics and Game Studies Laboratory. His research spans three interconnected domains: Reinforcement Learning applied to game algorithm optimization Machine Learning for NLP/NLU tasks Speech Technology Integration combining ASR and TTS for human-computer interaction Prior to TED University, Dr. Ulu contributed to impactful projects at Hacettepe Technopolis, including: TÜBİTAK-funded SOBAG-111K030 (2012-2014) analyzing Turkish news corpus linkage AFAD disaster simulation project AFADSIM (2014-2017) modeling socioeconomic resilience

• Information Security and Cryptology
• Artificial Intelligence
• Computer Software
• +۳ مورد دیگر

Mehmet Ali YALÇINKAYA is a full-time Lecturer in the Department of Computer Engineering at Ahi Evran University's Faculty of Engineering and Architecture. His academic journey includes a PhD (2020) and MSc (2015) in Computer Engineering from Süleyman Demirel University, and a BSc in Electronics and Computer Education (2012). PhD: Computer Engineering, Süleyman Demirel University (2020) MSc: Computer Engineering, Süleyman Demirel University (2015) BSc: Electronics and Computer Education, Süleyman Demirel University (2012) His research focuses on Information Security and Cryptology , Artificial Intelligence , Computer Software , and Cybersecurity . Recent work includes phishing detection systems, deep learning applications for security, and AI-driven educational tools. Key article trends: 2025: NLP for violence detection in healthcare 2024: Multilayer security frameworks, AI in education, and biometric authentication 2023: Phishing defense mechanisms and agricultural pest detection He has supervised four Master's theses and collaborated with 48 researchers. Notable projects include a BCI-supported deep learning model for motor/facial analysis (2025-2027) and AI-powered educational systems for primary schools.