Find Academic Supervisors

• Natural Language Processing
• Computational Linguistics
• Dependency Parsing
• +3 more

Prof. Dr. Gülşen Eryiğit is a Professor at Istanbul Technical University within the Faculty of Computer and Informatics , Department of Artificial Intelligence and Data Engineering . She founded and directs the ITU Natural Language Processing Group , Turkey's leading team in Turkish-language NLP, and serves as Senior Action Editor for ACL RR , Director of ITU TÖMER (Turkish Language Teaching Center), and Co-Chair of the EU UniDive Cost Action WG3. Education: PhD in Computer Engineering from ITU (2007), MSc and BSc from ITU and Marmara University Research: Focuses on Natural Language Processing for Turkish, including dependency parsing , coreference resolution , multiword expressions , and language education technology Her recent work involves multilingual transfer learning , LLM applications for Turkish text simplification, and gamification for morphology education. She has received prestigious awards like the Siemens Excellence Award and TÜBİTAK's Above Threshold Award . Her research has produced 65+ publications and 29+ projects funded by EU, TÜBİTAK, and industry partners. Scientific Awards: Siemens Excellence Award (2007) Above Threshold Award (TÜBİTAK, 2015) Certificate of Appreciation (EU 7th Framework, 2012) Thank You Plaque (ITU, 2017) The ITU NLP Group under her leadership has developed Turkey's first licensed NLP software exported internationally. She collaborates with European institutions through COST actions and participates in ACL, CoNLL, and LREC conferences. Her lab focuses on language technology for Turkish , including sign language processing and social media normalization.

Ahmet AcarView profile

Associate Professor

• Cancer Biology
• Precision Medicine
• Drug Resistance Mechanisms
• +3 more

Dr. Ahmet Acar is an Associate Professor at the Department of Biological Sciences, Middle East Technical University (METU), Ankara, Turkey. He leads the Cancer Precision Medicine and Drug Resistance Laboratory, focusing on understanding mechanisms of drug resistance in cancer. His research integrates experimental models, next-generation sequencing, and deep learning to address clinical challenges in cancer therapy. Dr. Acar holds a B.Sc. from METU's Biological Sciences department and a Ph.D. from the Cancer Research UK Manchester Institute. He completed postdoctoral training at the Institute of Cancer Research, London, and the University of Manchester. Research Interests: Drug resistance mechanisms, precision oncology, tumor microenvironment modeling, patient-derived organoids, computational pathology, and evolutionary cancer biology. His lab develops 2D/3D co-culture systems, PDO biobanks, and AI-driven histopathology tools to improve treatment strategies. Recent Work Trends: Recent publications emphasize tumor evolution modeling, matrix mechanics in drug resistance, and AI applications in histopathology. Collaborations with hospitals in Turkey and Europe support PDO biobank initiatives. His team explores evolutionary steering strategies to exploit collateral drug sensitivities. Labs/Teams: Precision Medicine and Drug Resistance Lab at METU focuses on interdisciplinary approaches combining wet-lab experiments with computational methods. Current projects include ex vivo tumor modeling and AI-driven diagnostic tools for oncology.

Umut AydemirView profile

Associate Professor

• Boron-Based High-Tech Materials
• Laser Crystals
• Materials Displaying Unusual Properties
• +10 more

Umut Aydemir is an Associate Professor at the Department of Chemistry, Koç University , where he also serves as Director of KUBAM (Koç University Boron Application and Research Center) . His research focuses on Boron-Based High-Tech Materials, 2D Materials, Electrocatalysis, Thermoelectric Energy Harvesting , and Hydrogen Storage . PhD in Chemistry (2012), Dresden University of Technology MSc in Materials Science and Engineering (2006), Koç University BSc in Chemistry and Physics (2004), Koç University Umut’s work addresses structure-property relationships in advanced materials, with a particular emphasis on boron-containing compounds , MXenes , and thermoelectric systems . His recent publications highlight innovations in electrocatalytic water splitting , hydrogen storage materials , and sustainable coating technologies . His research trends include: Development of metal diborides for water splitting Engineering MXene/polymer composites for corrosion resistance Novel approaches to thermoelectric materials like MgAgSb and Zintl phases Designing hydrogenated borophene for environmental applications Awarded the 2024 Koç University College of Science Outstanding Faculty Award and 2019 TÜBA Young Scientist Award , Umut leads high-impact projects in materials science. He advises graduate students in Nanocatalysis and Advanced Material Synthesis and contributes to interdisciplinary initiatives at KUBAM.

• Mechanical Engineering
• Finite Element Methods
• Materials Science
• +7 more

Mine Uysal is a Researcher at Yıldız Technical University, Faculty of Mechanical Engineering, Department of Mechanical Engineering. Her academic journey includes a BSc (2005), MSc (2010), and PhD (2015) in Mechanical Engineering from Pamukkale University and Yıldız Technical University, respectively. She began postdoctoral research at the University of Kentucky (College of Engineering) in 2017. PhD, Mechanical Engineering (2015, Yıldız Technical University) MSc, Mechanical Engineering (2010, Pamukkale University) BSc, Mechanical Engineering (2005, Pamukkale University) Her research focuses on advanced materials behaviors, including functionally graded materials, polymers, adhesively bonded joints, and finite element modeling for engineering systems. Key areas include composite structures, thermal/mechanical loading effects, and sustainable manufacturing techniques like nanofluid-assisted machining. Scientific contributions include 15 recent articles spanning topics such as delamination analysis in bonded beams, sustainable machining optimization, buckling behavior of graded polymers, and fracture mechanics in composite materials. Her work integrates finite element methods with experimental validation for adhesive joints and sandwich structures. Collaborative projects include research funded by The Scientific and Technological Research Council of Turkey (TUBITAK) and international postdoctoral work at the University of Kentucky.

Ozcan OzturkView profile

Professor

• Heterogeneous Computing
• Hardware Accelerators
• GPU Computing
• +7 more

Ozcan Ozturk is a Professor in the Computer Science and Engineering and Electronics Engineering programs at Sabancı University's Faculty of Engineering and Natural Sciences. Previously, he held professorships at Bilkent University and adjunct roles at North Carolina State University. His expertise spans heterogeneous computing, parallel systems, processor architecture, and compiler optimization. He earned his Ph.D. from Penn State University, with prior academic roles at the University of Florida and internships at Intel and Marvell. Education: Ph.D. in Computer Science and Engineering (2007), Pennsylvania State University M.S. in Computer Engineering (2002), University of Florida B.Sc. in Computer Engineering (2000), Bogazici University Research interests include accelerator technologies, GPU-based systems, multicore processors, and compiler optimizations. His work focuses on improving parallelization efficiency, energy optimization, and reliability in heterogeneous architectures. He leads funded projects like 'Machine Learning for Compiler Flags' and 'Graph Accelerator Design'. Notable awards include the Bilkent Teaching Award (2019), BAGEP (2018), and HiPEAC Paper Award (2016). He serves on editorial boards of IEEE and ACM journals and chairs major conferences like ASPLOS and ICS. Grants and collaborations include partnerships with Huawei, Intel, NVIDIA, and TÜBİTAK. He advises over 20 students and has supervised projects in safety-critical systems, FPGA accelerators, and compiler-directed optimizations. His lab develops domain-specific architectures, including RISC-V extensions for graph processing.

Erkut ErdemView profile

Professor

• Computer Vision
• Machine Learning
• Visual Saliency Prediction
• +5 more

Erkut Erdem is a Professor in the Department of Computer Engineering at Hacettepe University, where he leads the Computer Vision Laboratory (HUCVL). His research focuses on computer vision and machine learning, particularly on incorporating different kinds of context (spatial, temporal and cross-modal) into visual processing across all levels from low to high-level vision. He received his Ph.D. (2008), M.Sc. (2003), and B.Sc. (2001) from Middle East Technical University. Prior to joining Hacettepe University in 2010, he completed a post-doctoral fellowship at Ecole Nationale Supérieure des Télécommunications (2009-2010) and held visiting researcher positions at UCLA (2007) and Virginia Tech (2004). His current research interests include Visual Saliency Prediction, Automatic Image Description, Video/Photoset Summarization, Image Filtering, and Image Editing. Recent work has focused on multimodal learning with video-language models, diffusion-based image editing, and event-based vision for low-light conditions. His research has been published in top venues including NeurIPS, ICLR, ICCV, SIGGRAPH, and ACL. He has received significant recognition including The Young Researcher Award from Turkish Academy of Sciences and being named a 2022 Outstanding Associate Editor of IEEE Transactions on Multimedia. He has secured multiple research projects funded by TUBITAK and received gift funds from Adobe Research for text-guided image synthesis work. Current Teaching: BBM202: Algorithms, AIN434/BBM444: Fundamentals of Computational Photography Graduate Supervision: 6 current Ph.D. students, numerous recent graduates including Burak Ercan (2024) and Aysun Kocak (2023) Professional Affiliations: Co-affiliated with Koç University and İş Bank AI Center (KUIS AI)

• Computational Design
• Architectural Education
• Artificial Intelligence in Architecture
• +9 more

Assoc. Prof. Dr. Sema Alaçam Doğan has been affiliated with Istanbul Technical University since 2014, serving as an Associate Professor in the Department of Architecture . She has held administrative roles including Deputy Head of Department and Erasmus Coordinator. Education : PhD in Informatics in Architectural Design (2008-2014), MS in Informatics in Architectural Design (2005-2008), and BS in Architecture (1999-2005) from Istanbul Technical University. Her research explores Computational Design , Artificial Intelligence in Architecture , and Sustainable Material Innovation . She investigates digital tools for heritage preservation, daylight optimization in BIM, and cognitive development in architecture students. Recent publications analyze AI-assisted design literacy , machine learning for Sinan mosques , and environmental comfort in Harran houses . Her work integrates algorithmic frameworks with sustainable practices. Scientific awards include multiple ITU Publication and Performance Awards (2021-2024), FABFEST Prizes , and the 2024 Artemis Educator Award from NASA. Active projects like "Physical Computation in Architectural Drawing" and "Robotic Fabrication with Recycled Wind Turbine Blades" demonstrate her leadership in computational and sustainable research.

• Sustainable Machining Processes
• Tool Wear Monitoring
• Surface Roughness Optimization
• +3 more

Doç. Dr. Muhammed Aras is an Associate Professor in the Department of Mechanical Engineering at Baskent University (Başkent Üniversitesi), with a research focus on sustainable machining processes, tool wear monitoring, and surface roughness optimization. His work spans advanced manufacturing technologies, energy storage systems, and biomedical device design. PhD in Manufacturing Engineering (2018), Gazi Üniversitesi MSc in Mechanical Education (2013), Gazi Üniversitesi BSc in Mechanical Engineering (2010), Tabriz Islamic Azad University Research interests include sustainable machining (dry/hard turning, cooling-lubrication strategies), tool wear analysis (CBN, ceramic and coated inserts), and surface integrity optimization using AI-based methods (firefly algorithm, TOPSIS, Grey Relational Analysis). His 15 most recent articles (2017-2024) examine topics like: Surface roughness prediction in dry hard turning Energy storage technology viability assessments Cutting parameter optimization for various steels Acoustic/vibration monitoring in machining He has received scientific awards including the Teşvik Ödülü (Encouragement Award, 2015). His patent on an automatic orthognathic surgery articulator and book chapters on tool monitoring systems demonstrate his multidisciplinary impact.

• Computer Learning
• Fuzzy Sets and Systems
• Decision Support Systems
• +3 more

Prof. Sadettin Emre Alptekin is a full Professor of Industrial Engineering at Galatasaray University, Faculty of Engineering and Technology, where he also serves as Vice Dean. Since joining the university as a research assistant in 2000, he has steadily advanced through the academic ranks, becoming an Assistant Professor (2006–2010), Associate Professor (2010–2023), and finally Professor in 2023. Education: PhD (Dr), Industrial Engineering, Istanbul Technical University, Institute of Science and Technology, 2001–2006 MSc, Industrial Engineering, Galatasaray University, Faculty of Engineering and Technology, 1999–2001 BSc, Industrial Engineering, Istanbul Technical University, Faculty of Management, 1995–1999 Languages: Advanced English (C1), Upper-Intermediate French (B2), Advanced German (C1) Research Interests: Prof. Alptekin’s research focuses on Computer Learning , Fuzzy Sets and Systems , and Decision Support Systems . His work integrates artificial intelligence, machine learning, and soft-computing techniques to solve complex industrial and managerial problems in areas such as supply chain management, quality function deployment, blockchain adoption, and mental-health prediction. Publication Trends: Across more than 50 refereed publications, Prof. Alptekin has consistently explored hybrid intelligent models that combine fuzzy logic, machine learning, and multi-criteria decision-making. Recent articles emphasize deep-learning-based anomaly detection in industrial time-series data, blockchain adoption in supply chains, and machine-learning applications in subjective well-being and mental-health modeling. Scientific Awards & Honors: No specific awards or medals are listed in the provided documents. Research Leadership & Funding: Since 2008 he has been the principal investigator (executive) of 12 nationally funded projects, covering topics such as Industry 4.0 sub-system design, Internet of Things applications, artificial neural networks in organizational decision-making, big-data analytics, and strategic decision processes. Graduate Advising: He has formally supervised at least 8 master’s theses and numerous undergraduate projects. Representative thesis titles include Gaussian-process-regression-based man-hour prediction, machine-learning-driven human-behavior modeling, recommender-system design for e-commerce, thyroid-nodule diagnosis from scintigraphic images, software-effort estimation via neural networks, spreadsheet heuristics for joint-replenishment problems, cross-selling decision systems in insurance, and profitability analyses of Turkish banks under disinflation. Laboratories & Teams: While no dedicated laboratory name is disclosed, his continuous role as Vice Dean and principal investigator implies active leadership of the Industrial Engineering department’s research clusters in intelligent systems and decision support technologies.

• Deep Learning
• Self-supervised learning
• Efficient training of deep learning methods
• +3 more

Mustafa Taha Koçyiğit is a Full-time Assistant Professor at Bogazici University. His research focuses on Deep Learning, Self-supervised learning, Efficient training of deep learning methods, Computer vision, Efficient training of large language models, and Language grounded vision models. His recent work addresses computational efficiency in training methods and novel applications of deep learning across domains like aerospace defect detection and computer vision. His publications span advancements in self-supervised learning strategies (2023), efficient training for computer vision tasks (2023), and theoretical contributions like unsupervised batch normalization (2020). The 2025 work demonstrates cross-disciplinary impact in aerospace engineering through AI-driven defect detection via X-ray tomography. Notable Contributions: Bridging efficiency and accuracy in deep learning pipelines Technical Strengths: Neural architecture design, optimization strategies, and domain-specific model adaptation

Fatih EcevitView profile

Professor

• Numerical Analysis
• Partial Differential Equations
• Scattering Theory
• +2 more

Fatih Ecevit is Full Professor of Mathematics at Boğaziçi University, serving as Vice Chair of the Mathematics Department. Former research associate at Max-Planck-Institut für Mathematik in den Naturwissenschaften, Leipzig (2005-2007). Research develops computational methods for high-frequency scattering problems, including: Boundary element methods for wave propagation Asymptotic analysis of scattering phenomena Galerkin formulations for integral equations Lattice sum evaluations in graph theory Principal investigator for TÜBİTAK-funded project: 'Hybrid integral equation methods for high-frequency scattering problems' (2017-2020). Teaches graduate and undergraduate courses in numerical analysis, partial differential equations, and real analysis.

Veysel Murat İstemihan GençView profile

Professor

• Power Systems
• Transient Stability Assessment
• Machine Learning in Power Systems
• +7 more

Veysel Murat İstemihan Genç is a Professor in the Department of Electrical Engineering at Istanbul Technical University (ITU), College of Engineering. His research is centered on modern power systems, with a focus on transient stability, cybersecurity, and integration of renewable energy sources. He actively leads multiple research projects and supervises graduate students in advanced power system technologies. Research Interests: His work spans key areas including transient stability assessment, machine learning applications in power systems, cyber-attack detection in AGC systems, and dynamic security evaluation under high renewable penetration. He employs cutting-edge techniques such as ensemble learning, deep neural networks, and hybrid optimization algorithms. Publication Trends: Recent publications (2023–2025) highlight a strong trend toward integrating AI and machine learning for real-time transient stability prediction, cybersecurity in distributed energy systems, and performance optimization of solar and wind-integrated grids. His work frequently addresses challenges in low-inertia systems and false data injection attacks. Scientific Projects: Strengthened Machine Learning-Based Dynamic Security Evaluation for Transient Stability under False Data Injection Attacks (BAP, 2025) Analysis and Control Methods for Stability of Large-Scale Low-Inertia Power Systems (BAP, 2023–2024) Dynamics Security Evaluation of Renewable-Rich and Cyber-Attacked Power Systems (BAP, 2022–2024) Risk-Based Stability Assessment and Corrective Control Methods in Power Systems (BAP, 2019–2022) Wide-Area Monitoring Protection and Control System Design Using Advanced Signal Processing and Machine Learning (TÜBİTAK, 2018–2020) Advising and Grants: He is the principal investigator (PI) on multiple funded research projects from BAP and TÜBİTAK, indicating strong grant acquisition and leadership. His supervision of 27 ongoing theses reflects an active role in mentoring graduate students in electrical engineering and power systems. Labs and Teams: While specific lab names are not mentioned, his projects suggest leadership in a research group focused on smart grid technologies, AI-enabled power system security, and renewable integration at Istanbul Technical University.

Handan KulanView profile

Assistant Professor

• Bioinformatics
• Computational Biology
• Machine Learning
• +3 more

Handan Kulan serves as Assistant Professor at Yeditepe University's Faculty of Computer and Information Sciences, Department of Information Systems and Technologies since 2024. Previously, she held faculty positions at Istinye University (2023), Uskudar University (2022), and Beykoz University (2020) across computer engineering and software engineering departments. Education: Ph.D. in Computer Engineering, Kadir Has University (2016-2020): Thesis on critical proteins in Down syndrome learning processes M.S. in Computer Science and Engineering, Sabanci University (2013-2014): Thesis analyzing protein residue networks B.S. in Genetics and Bioengineering, Yeditepe University (2007-2013) Second Major in Computer Engineering, Yeditepe University (2009-2013) Her research integrates machine learning with biomedical challenges, specializing in Down syndrome proteomics, neural network analysis of brain aging, and immune system disorders. She develops computational models for protein identification and applies gradient boosting algorithms to biological datasets, bridging AI with healthcare decision systems as demonstrated in her 2023 Springer book. Publications reveal consistent focus on computational approaches to Down syndrome, with recent conference presentations expanding into statistical clustering of biological data and gene ontology analysis for drug discovery. Her work demonstrates methodological evolution from protein network analysis to advanced predictive analytics. Awards: No specific scientific awards documented in source materials. Dr. Kulan actively supervises graduate theses while teaching core computer science courses including Deep Learning, Artificial Intelligence, and Data Structures at both undergraduate and graduate levels across multiple institutions. Her teaching portfolio reflects direct alignment with her research in AI-driven biomedical analysis.

Onur VarolView profile

Assistant Professor

• Computational Social Science
• Network Science
• Machine Learning
• +4 more

Onur Varol is an Assistant Professor at Sabanci University's Computer Science Department and leads the VIRAL Lab, which focuses on computational social science, network science, and machine learning. He has affiliations with the Center of Excellence for Data Analytics. His research spans social bot detection, misinformation analysis, and online behavior modeling.

• Artificial Intelligence
• Quantum Computing
• Theoretical Computer Science
• +3 more

Cem Say is a Professor in the Department of Computer Engineering at Boğaziçi University's Faculty of Engineering, where he has established himself as a leading researcher in theoretical computer science and artificial intelligence. His academic journey began with the completion of his doctoral dissertation titled Qualitative System Identification in 1992, which was the first thesis of Boğaziçi University's Computer Engineering PhD program. Professor Say's research interests span multiple domains of computer science, with significant contributions to quantum computing, artificial intelligence, and theoretical computer science. His early work focused on qualitative reasoning and simulation, particularly through the QSIM algorithm, where he made significant improvements to filtering techniques and addressed challenges in representing physical systems. Over time, his research evolved toward quantum computation, where he has made substantial contributions to quantum finite automata theory, space-bounded quantum computation, and quantum complexity classes. His recent work explores the energy complexity of computation, bridging theoretical computer science with thermodynamics. His publication record shows a clear evolution from classical AI and qualitative reasoning toward quantum computation. The most recent articles demonstrate his focus on space-bounded quantum computation, energy complexity of regular languages, and interactive proof systems with minimal resources. His work consistently addresses fundamental questions about computational limits, particularly in quantum and sublogarithmic-space models. Professor Say has also made significant contributions to science communication through several books written for general audiences, including 50 Soruda Yapay Zekâ (2018), Yeni Dünya, Yeni Ağ (2020), and En Hakiki Mürşit (2021), which explain complex concepts in artificial intelligence and scientific methodology in accessible terms. Throughout his career, Professor Say has been actively involved in the Turkish academic community, editing proceedings for multiple Turkish symposia on artificial intelligence and neural networks. His doctoral dissertation established foundational work in qualitative system identification, and his subsequent research has consistently pushed boundaries in theoretical computer science, particularly in quantum computation where he has collaborated extensively with Abuzer Yakaryılmaz and other researchers.