Find Academic Supervisors

• Artificial Intelligence
• Machine Learning
• Data Mining
• +5 more

Prof. Dr. Mehmet Reşit Tolun is a full-time Professor in the Department of Software Engineering at Çankaya University (Turkey) since 2022. Previously held full-time professor positions at Konya Food and Agriculture University (2020-2022), Aksaray University (2013-2017), and TED University (2011-2013), along with a part-time professorship at Başkent University (2017-2020). Specializes in Artificial Intelligence , Machine Learning , and Data Mining , with a focus on deep learning applications in aerospace, biomedical data analysis, and software process improvement. PhD in Computer Science (University of Kent, 1985) MSc in Computer Science (University of Kent, 1982) BSc in Physics and Computer Science (University of Kent, 1981) Research Interests span deep learning frameworks, hybrid expert systems, software engineering methodologies, and biomedical signal processing. Publications emphasize practical implementations in medical diagnostics, robotics, and agricultural pest detection. Scientific Awards include the IEEE Third Millenium Medal (2000). Supervised over 55 graduate students, including Burak Çetin, Uğur Özotuk, and Mahinur Doğan. Collaborated with researchers from Orta Doğu Teknik Üniversitesi , Çankaya University , and Aksaray University .

Figen S. OktemView profile

Associate Professor

• Computational Imaging
• Physics-informed Machine Learning
• Inverse Problems
• +5 more

Figen S. Oktem is an Associate Professor in the Department of Electrical and Electronics Engineering at Middle East Technical University (METU), Ankara, Turkey. Her research focuses on advanced imaging systems and algorithms, including computational imaging, inverse problems, and machine learning applications in signal processing. Ph.D., Electrical and Computer Engineering, University of Illinois at Urbana-Champaign (UIUC) (2014) M.S., Electrical and Electronics Engineering, Bilkent University (2009) B.S., Electrical and Electronics Engineering, Bilkent University (2007) Her work bridges physics-informed machine learning and computational optics, with applications in spectral imaging, radar, and microwave systems. She has also explored Fourier phase retrieval, denoising diffusion models, and real-time MIMO radar imaging. Recent publications highlight trends in phase retrieval using deep learning ( prNet , I2I-PR , DDRM-PR ), 3D MIMO imaging, and compressive spectral imaging. Techniques often integrate plug-and-play regularization, stochastic refinement, and physics-based priors. She can be contacted via email at figeno@metu.edu.tr . Curriculum vitae and publications are accessible through her Google Scholar profile.

Cevdet AykanatView profile

Professor

• Parallel Computing
• High-Performance Computing
• Graph and Hypergraph Partitioning
• +4 more

Prof. Cevdet Aykanat is a Professor of Computer Engineering at Bilkent University, Ankara, Turkey. He earned his BS/MS in Electrical Engineering from METU and PhD from Ohio State University as a Fulbright scholar. His research focuses on parallel computing, sparse matrix algorithms, graph partitioning, and high-performance computing for big data. He has been affiliated with Bilkent since 1989 and has held roles like Associate Provost. Education: BSc/MSc (METU Electrical Engineering), PhD (Ohio State University Electrical & Computer Engineering). Research interests include parallel scientific computing, combinatorial optimization, distributed systems, and large-scale data analysis. His work spans over 100 publications in top journals like IEEE Transactions and SIAM, with 5,500 citations and an H-index of 40. Awards: 1996 TUBITAK Investigator Award, 2007 METU Parlar Science Award. He led 6 TUBITAK projects and participated in EU-funded projects like PRACE-1IP to PRACE-6IP. Publications emphasize efficient parallel algorithms for sparse computations, graph partitioning, and distributed systems. His work addresses latency reduction, load balancing, and scalable data processing in HPC environments. Grants: Funded by TUBITAK, Intel SSD, and EU programs. Academic service includes editorial roles at IEEE Transactions on Parallel and Distributed Systems.

• Stochastics
• Probability theory
• Stochastic processes
• +7 more

Lasse Leskelä is an Associate Professor at the Department of Mathematics and Systems Analysis at Aalto University. His research focuses on stochastics, particularly statistical network models, random graphs, and stochastic processes acting on networks. He aims to derive mathematical laws for predicting macroscopic behavior in large random systems and improving statistical learning algorithms for high-dimensional data analysis. Probability theory Stochastic processes Random graphs Statistical network models Queueing Theory Applied probability Leskelä's work spans theoretical and applied domains, with applications in information networks, social networks, biological systems, and financial modeling. His research group actively explores models like random intersection graphs, stochastic block models, and graphons to understand complex systems. Leskelä's recent publications in journals such as the IEEE Transactions on Network Science and Engineering and PLoS Computational Biology highlight his contributions to community detection, hypergraph connectivity, and epidemic modeling. His work bridges mathematical theory with practical applications in data science and network analysis. Varma-Sampo MSc thesis prize , Finnish Mathematical Society McKinsey Prize for the best MSc degree completed in 1999 at the Helsinki University of Technology Leskelä has collaborated with leading researchers across disciplines. His research includes grants from Aalto University and international institutions, focusing on advancing stochastic modeling and data analysis techniques.

• Digital Signal Processing
• Image Processing
• Computer Vision
• +2 more

Mehmet Türkan serves as an Associate Professor in the Department of Electrical and Electronics Engineering at the Faculty of Engineering, Izmir University of Economics, Turkey, where he has held academic positions since 2015 including promotion from Assistant to Associate Professor. His academic credentials include: PhD in Computer Science from Université Rennes I, France (2011) MSc in Electrical and Electronics Engineering from Bilkent University, Turkey (2007) BSc in Electrical and Electronics Engineering from Eskişehir Osmangazi University, Turkey (2004) Dr. Türkan's research centers on digital signal/image processing, computer vision, and machine learning with emphasis on sparse representations, linear embeddings, and deep learning applications. His work bridges theoretical algorithm development with practical implementations in biomedical imaging and multimedia systems, particularly advancing techniques for image fusion, quality assessment, and medical diagnostics. Analysis of his 2020-2024 publications reveals a strong trajectory toward biomedical applications—especially melanoma detection and fluorescence microscopy—while maintaining core contributions to image processing fundamentals. This period shows increasing integration of deep learning with traditional signal processing methods, reflecting broader field trends. His scientific recognition includes: Best Student Paper Award at IEEE ICIP 2010 Dr. Türkan has secured research funding through European Commission initiatives including MUSCLE-NoE, 3-DTV-NoE, and UltraHD-4U projects. His collaborative work spans international teams at Technicolor Research & Innovation Center (2011-2015) and current academic partnerships. He maintains active leadership in image processing research through ongoing publications and supervision of graduate projects at Izmir University of Economics, with recent work focusing on spatio-temporal information preservation and medical image analysis.

• Fluid Mechanics
• Coastal Sciences and Engineering
• Numerical Modeling
• +15 more

Cihan Bayındır is an Associate Professor at Istanbul Technical University's Faculty of Civil Engineering, Department of Civil Engineering, specializing in fluid mechanics, coastal engineering, and computational methods. His research integrates advanced mathematical techniques with practical engineering applications across multiple disciplines. His primary research areas include Fluid Mechanics, Coastal Sciences and Engineering, Numerical Modeling, Fluid Physics, Acoustics and Vibrations, Nonlinear Dynamics, and Quantum Hydrodynamics. Bayındır's work demonstrates a strong interdisciplinary approach, bridging traditional civil engineering with cutting-edge computational methods and quantum phenomena. His research has significant applications in coastal protection, renewable energy systems, disaster management, and infrastructure resilience. Analysis of his recent publications reveals a clear trend toward applying artificial intelligence and machine learning techniques to traditional civil engineering challenges. His work increasingly focuses on using compressive sensing, deep learning (particularly LSTM networks), and fuzzy logic systems to solve complex problems in wave dynamics, vibration analysis, and tsunami prediction. The integration of quantum computing concepts with classical fluid mechanics represents another distinctive aspect of his research portfolio. ITU Doctoral Special Award-2024-Advisor (2025) ITU 2023 Academic Performance Award (2024) Turkish Academy of Sciences (TUBA) Outstanding Young Scientist Award (GEBİP) (2022) Elsevier Frontiers Article Award (2020) Kaleidoscope Award (American Physical Society, 2016) Bayındır leads multiple research projects funded by ITU's Scientific Research Projects Unit (BAP), including studies on dam break wave propagation using fractional equations, optimization of wave energy concentrators with AI methods, and three-dimensional vibration control in marine structures. His work involves collaborations with international institutions, including previous engagement with CERN on accelerator design. He maintains an active research laboratory focused on hydraulics and coastal engineering, with specialized equipment for wave and vibration analysis in the Hydraulics Laboratory HL 216 at ITU.

• Machine Learning
• Image Processing
• Bioinformatics
• +7 more

Abdulkerim Çapar is an Assistant Professor in the Faculty of Computer and Informatics at Istanbul Technical University, where he is affiliated with the Department of Computer Science. His research lies at the intersection of machine learning, image processing, and bioinformatics, with a strong emphasis on biomedical applications including digital pathology and neuroscience. His educational background includes a PhD in Computer Engineering from Istanbul Technical University, along with earlier degrees in Electronics and Communication Engineering from the same institution. He has also been a research assistant at both Istanbul Technical University and Wake Forest University Center for Artificial Intelligence. Dr. Çapar's research interests center on developing machine learning and computer vision methods for medical image analysis, particularly in fluorescence microscopy, myelin quantification, cervical and breast cancer diagnostics, and calcium imaging for neurological disorders. His work contributes to UN Sustainable Development Goals in health and innovation. His recent publications (2020–2024) reflect a strong trend in interpretable AI for histopathology, automated scoring systems for cancer biomarkers, and segmentation techniques for neural structures. He has published in high-impact journals such as PLoS ONE , IEEE JBHI , and Biomedical Signal Processing and Control . Scientific Awards: Dr. Akın Çakmakcı Thesis Award, Türkiye Technology Development Foundation, 2012 Memberships: Turkish Brain Research and Neurosciences Association (TÜBAS), 2019–present Grants and Projects: He has led or contributed to research on automated myelin detection, digitization of herbarium collections, and AI-assisted cervical cytopathology. He holds a patent for a method and system for automated myelin detection in fluorescence microscopy using machine learning. Labs and Collaborations: Dr. Çapar collaborates with interdisciplinary teams in neuroscience and medical imaging, including researchers from Wake Forest University and other Turkish institutions. His work involves close collaboration with medical experts in pathology and neurology.

SEMA KAYHANView profile

Professor

• Image Processing
• Compressive Sensing
• Signal Processing
• +11 more

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

MEHMET DEMİRView profile

Assistant Professor

• Electrical Engineering
• Signal Processing
• Image Processing
• +3 more

Mehmet Demir is an Assistant Professor at Gaziantep University, Faculty of Engineering, specializing in Electrical and Electronics Engineering. His research focuses on signal processing, image processing, and machine learning with applications in synthetic aperture radar (SAR) imaging and compressed sensing. Education: PhD (2013-2019), MSc (2009-2012), and BSc (2004-2009) in Electrical and Electronics Engineering at Gaziantep University Academic Positions: Assistant Professor (2021-present), Research Assistant (2013-2021), and Research Assistant (2009-2013) at Gaziantep University Demir's research explores advanced algorithm development for SAR imaging systems, particularly in handling quantized data and signal-translation errors. His work leverages computational methods like ADMM, FISTA, and spectral conjugate gradient techniques, with applications in remote sensing and signal reconstruction. Recent publications focus on 1-bit compressive sensing in SAR imaging (2018, 2019, 2020, 2021), demonstrating his ongoing contributions to data-efficient image reconstruction algorithms. His research has received recognition through TÜBİTAK Publication Incentive Awards in 2019 and 2014. Demir actively engages in scientific communities as a member of TMMOB-EMO (Chamber of Electrical Engineers) since 2009, and contributes to engineering education with courses like EEE439 Introduction to Remote Sensing, emphasizing practical algorithm development in MATLAB/Python.

Ahmet DuranView profile

Professor

• Mathematics
• Applied Mathematics
• Wind Energy
• +3 more

Professor Ahmet Duran at Istanbul Technical University's Department of Mathematics specializes in applied mathematics with interdisciplinary applications in renewable energy systems, financial mathematics, and computational modeling. His research focuses on differential equations, numerical methods, and optimization techniques. Current projects involve EU collaborations on supercomputing (GPGPU for bio-fluid applications) and sparse direct solvers for linear equations. Recent publications emphasize wind/solar energy prediction, climate resilience, and financial portfolio optimization via principal component analysis. Technical expertise spans spectral analysis, fuzzy logic modeling, and high-performance computing using frameworks like SuperLU.

• High Performance Computing (HPC)
• Data Locality
• GPU Computing
• +7 more

Dr. Didem Unat is an Associate Professor at Koç University, College of Engineering, Department of Computer Engineering . Her research focuses on High Performance Computing (HPC) , GPU Computing , and Compiler Design , with particular emphasis on data locality optimization, multi-GPU communication, and adaptive mesh refinement. She leads the Parallel and Multicore Computing Lab , driving innovations in exascale co-design and heterogeneous system optimization. ACM SIGHPC Emerging Woman Leader in Technical Computing Award (2021) ERC Starting Grant (1.5M EUR) for the SparCity Project (2025) EuroHPC Grant for Extreme Scale Technologies and Applications (2025) 2025 BAGEP Award (Science Academy) Her work bridges Computer Architecture and Machine Learning , addressing challenges in data locality , multi-GPU systems , and performance portability . Recent publications highlight trends in sparse tensor optimization , asynchronous AMR execution , and compiler support for heterogeneous memory . She has developed tools like ComScribe for intra-node GPU communication analysis and ReuseTracker for multicore reuse distance analysis. Dr. Unat’s research is supported by grants from ERC and EuroHPC , reflecting her leadership in exascale technologies and GPU-centric systems . She contributes to the evolution of tiling abstractions , dynamic LLM training , and lightweight communication detection for modern architectures.

Lale AkarunView profile

Professor

• Biometrics
• 3-D Face Recognition
• Hand Vein Recognition
• +8 more

Lale Akarun is Professor of Computer Engineering at Boğaziçi University, Istanbul, where she also serves as Director of the TETAM research center. She leads the Media Laboratory (MEDIALAB) and the Perceptual Intelligence Laboratory (PILAB), focusing on biometrics, sign-language recognition, computer vision and image processing. Research Interests: Biometrics: 3-D Face & Hand-vein Recognition Gesture & Sign-Language Recognition for HCI Computer Vision, Image Processing & Graphics Her recent work (2023-2025) targets robust sign-language translation via self-supervised learning, multi-cue skeleton fusion, cross-dataset transfer and attention modeling, while also contributing new benchmarks for 3-D object reconstruction and occlusion-aware human-pose estimation. Laboratories & Teams: Media Laboratory (MEDIALAB) Perceptual Intelligence Laboratory (PILAB) She advises graduate researchers and has released influential datasets such as BosphorusSign22k, fostering advances in Turkish sign-language recognition and low-resource transfer learning.

• Blind Signal Processing
• Bayesian Estimation
• Particle Filters
• +3 more

Ayşın Baytan Ertüzün is a Professor at Boğaziçi University, specializing in signal processing and statistical methodologies. Their research focuses on blind signal processing, Bayesian estimation techniques, particle filters, and seismological applications. They have contributed extensively to defect detection in textiles using advanced signal processing methods like wavelet transforms and independent component analysis, as well as geophysical signal analysis through Bayesian deconvolution approaches. Over 25 years of academic work includes pioneering work in non-Gaussian processes, time-varying autoregressive models, and particle filtering algorithms for complex signal environments. Their research bridges theoretical advancements with practical applications in manufacturing quality control and seismic data analysis. Key technical contributions include hybrid deconvolution methods for Bernoulli-Gaussian processes, subband domain subspace analysis for texture defects, and entropy-based 2D lattice filter structures. Their work often emphasizes robust statistical frameworks for non-stationary and non-Gaussian signal environments, with applications spanning from environmental forecasting (wind speed modeling) to medical signal analysis (respiratory sound coherence). Publications span from foundational 1990s lattice filter innovations to modern Bayesian approaches. Their methodologies are characterized by interdisciplinary fusion of signal processing theory with real-world industrial and geophysical challenges. No specific awards are listed in the provided materials, though their extensive publication record indicates sustained academic impact.

Kamer KayaView profile

Associate Professor

• Parallel Algorithms
• Machine Learning on Sparse Data
• Graph Algorithms
• +6 more

Kamer Kaya is an Associate Professor at the Faculty of Engineering and Natural Sciences, Sabancı University. He obtained his PhD in 2009 from the Department of Computer Science and Engineering at İhsan Doğramacı Bilkent University, focusing on Threshold Cryptography. His career spans research roles at CERFACS (France) and Ohio State University, where he worked in the HPC Lab and later as a Research Assistant Professor in the Dept. of Biomedical Informatics. Since 2014, he has been affiliated with Sabancı University and founded Dakik Software Technologies in 2018, focusing on R&D projects in blockchain, machine learning, and high-performance computing (HPC). He is actively involved in Sabancı University's Center of Excellence in Data Analytics and COMPUTE. PhD in Threshold Cryptography from Bilkent University (2009) Postdoctoral research at Ohio State University's HPC Lab Founder of Dakik Software Technologies (2018) His research interests include parallel algorithms , sparse data machine learning , graph algorithms , HPC hardware optimization , and cryptography . He collaborates on projects involving GPUs/IPUs for ML applications, blockchain interoperability, and digital twins for automation. As a team leader, he seeks students skilled in algorithms, data structures, and C/C++ programming.

Alper Kürşat UysalView profile

Associate Professor

• Text Classification
• Feature Selection
• Pattern Recognition
• +2 more

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.