Find Academic Supervisors

Özlem İlk DağView profile

Professor

• Longitudinal Data Analysis
• Multilevel Modeling
• Interactive Graphics
• +5 more

Prof. Dr. Özlem İlk Dağ is a Professor of Statistics at the Middle East Technical University (METU), Ankara, Turkey. She holds the position of Department Head of Statistics and serves on advisory boards for TUBITAK and the Journal of Biostatistics. Her academic journey includes roles from Research Assistant (1997) to Professor (2018), with affiliations in Actuarial Sciences and Biostatistics. Education: Ph.D. in Statistics (Iowa State University, 2004), M.S. and B.S. from METU. Research focuses on longitudinal data analysis, multilevel modeling, Bayesian methods, and biostatistics. She authored three editions of R Yazilimina Giris and a foundational book on multivariate longitudinal data analysis. Key contributions include developing marginalized transition random effects models (MTREM) and R packages like 'mmm'. Awards include METU's 20-year service recognition and teaching excellence. She has supervised numerous research projects and co-authored over 50 peer-reviewed articles across biostatistics, genomics, and statistical computing. Her work bridges statistical theory and application, with notable contributions to gene expression clustering, maternal antibody studies in veterinary science, and computational methods for longitudinal data. She maintains active collaborations in genomics and biostatistics, and her lab focuses on integrating statistical computing with biomedical research.

Sinan YıldırımView profile

Researcher

• Bayesian Statistics
• Monte Carlo Methods
• Data Privacy
• +3 more

Sinan Yıldırım is a Researcher in the Faculty of Engineering and Natural Sciences at Sabancı University, Turkey. His primary research focuses on Bayesian Statistics, Monte Carlo methods, and data privacy, with interdisciplinary applications in machine learning and signal processing. He holds a BSc and MSc in Electrical and Electronics Engineering from Boğaziçi University, followed by a PhD in Mathematical Statistics from the University of Cambridge. Postdoctoral research (2013-2015) at the University of Bristol’s School of Mathematics involved the EPSRC-funded project 'Intractable Likelihood: New Challenges from Modern Applications (i-like).' His work bridges theoretical statistics with practical problems in privacy, control systems, and energy optimization. Research interests emphasize Bayesian methodologies for privacy-preserving data analysis, dynamic modeling of complex systems, and stochastic optimization algorithms. Recent publications explore differential privacy in machine learning, Monte Carlo techniques for high-dimensional inference, and applications of Bayesian methods in robotics and energy systems. Advising and grants include contributions to multi-party resource sharing frameworks and privacy-aware algorithms. His work integrates computational methods with real-world challenges in engineering and policy modeling.

Berna KılınçView profile

Professor

• History and Philosophy of Probability Theory
• Kant’s Theoretical Philosophy
• Statistical Reasoning
• +5 more

Prof. Berna Kılınç is a faculty member in the Department of Philosophy at Boğaziçi University, Faculty of Letters. She holds a Ph.D. from the University of Chicago and serves as Department Chair and Fourth Year Advisor. Her academic work spans philosophy of science, logic, epistemology, and the history of science, with a particular focus on probability theory and Kantian philosophy. Ph.D., University of Chicago, USA Her research interests include the history and philosophy of probability, Kant’s theoretical philosophy, statistical reasoning, and informal logic. She explores foundational questions in scientific reasoning, objectivity, and the evolution of logical thought, often bridging historical analysis with contemporary philosophical debates. The recent publications of Prof. Kılınç reflect a sustained engagement with logic, epistemology, and the philosophy of science. Her work spans from Kantian metaphysics to feminist epistemology and statistical methodology, demonstrating a broad yet coherent trajectory centered on the nature of scientific and logical reasoning. She frequently publishes on Ottoman science, the philosophy of chance, and meta-theoretical issues in research synthesis. No scientific awards were mentioned in the provided text. Prof. Kılınç has editorial and translation experience, having co-edited volumes of the World Congress of Philosophy and several issues of Felsefe Tartışmaları . She has translated philosophical texts into Turkish, contributing to the dissemination of analytic philosophy in Turkey. While specific grant information is not provided, her publications in international journals and edited volumes suggest active research engagement. She advises senior students as the Fourth Year Advisor, indicating a role in academic mentoring. There is no mention of specific labs or research teams in the provided information.

Zeynep Işıl KalaylıoğluView profile

Professor

• Bayesian Statistics
• Environmental Statistics
• Health Statistics
• +3 more

Prof. Zeynep Işıl Kalaylıoğlu is a Professor of Statistics at the Department of Statistics, Middle East Technical University (METU), Ankara, within the Faculty of Arts and Sciences. She holds a Ph.D. in Statistics from North Carolina State University (2002) and has held roles including Associate Professor (2014–2022), Assistant Professor (2009–2014), and researcher at the National Cancer Institute (2002–2007). She has served in administrative roles such as Assistant to the Department Head and member of METU’s Wind Energy Research Center and Faculty Board. Ph.D.: Statistics, North Carolina State University, 2002 M.S.: Statistics with Computational Engineering minor, North Carolina State University, 1999 B.S.: Statistics with Computer Engineering minor, Middle East Technical University, 1995 Her research focuses on Bayesian statistical methodologies for environmental and health phenomena. Key areas include spatial/temporal modeling of atmospheric and ecological variables, prediction of migratory patterns (e.g., wind direction, bird movements), and risk modeling for breast cancer using mammogram data. She develops flexible models for circular and extremal data, emphasizing applications in ecology, epidemiology, and environmental science. Her recent publications address topics like goodness-of-fit tests for statistical distributions, predictive model selection for circular data, and Bayesian approaches for missing covariates in generalized linear models. Current projects include mobile apps for olive harvest optimization and frost预警 systems, reflecting her commitment to applied statistical solutions. Labs/Teams: She leads the BayeZian Research Group, collaborating with METU’s Ecosystem Research Center on interdisciplinary projects. Her teaching expertise spans mathematical statistics, Bayesian theory, and statistical inference at undergraduate and graduate levels.

Selin AslanView profile

Assistant Professor

• Applied Mathematics
• Computational Mathematics
• Imaging Science
• +2 more

Selin Aslan serves as an Assistant Professor in the Department of Mathematics at Koç University, Istanbul, Turkey, where she conducts research at the intersection of computational mathematics and imaging science. Her academic appointments and research activities are centered within the university's mathematics department, contributing to both undergraduate and graduate education in mathematical sciences. Her educational qualifications include: PhD in Mathematics from Virginia Polytechnic Institute and State University (2018) Master's in Mathematics from Rochester Institute of Technology (2013) B.A. in Mathematics from Ege University (2010) Dr. Aslan's research program focuses on developing advanced computational methods for solving inverse problems in imaging, with particular expertise in phase retrieval, tomographic reconstruction, and ptychography. Her work bridges theoretical mathematics with practical applications in medical imaging, microscopy, and materials science, emphasizing algorithmic innovation and computational efficiency. She integrates techniques from deep learning, optimization theory, and high-performance computing to address challenges in image reconstruction under physical constraints. Analysis of her publication record reveals a consistent trajectory toward solving complex imaging problems through hybrid approaches that combine physics-based models with data-driven techniques. Her recent work demonstrates increasing emphasis on scalability for large datasets, robustness in photon-limited scenarios, and real-time processing capabilities, with applications spanning biomedical imaging to advanced microscopy. No scientific awards were documented in the available sources. Information regarding student advising and research grant activities was not specified in the provided materials, though her publication record suggests active research collaboration. Her computational focus implies engagement with high-performance computing resources for large-scale image reconstruction tasks. While specific laboratory infrastructure details were unavailable, her research on multi-GPU implementations and distributed computing indicates utilization of advanced computational facilities for handling large-scale imaging datasets.

Erhan YumukView profile

Researcher

• Control Theory
• Fractional Order Systems
• Biomedical Engineering
• +3 more

Dr. Erhan Yumuk is a researcher at Ghent University and an alumnus of Istanbul Technical University , where he earned his Ph.D. in Control and Automation Engineering . He has served as a lecturer and deputy head of the department at Istanbul Technical University. His research focuses on: Fractional order control systems Anesthesia depth and hemodynamic control Pharmacokinetics/pharmacodynamics modeling Industrial control laboratory development Remote access control education Artificial intelligence in biomedical applications Recent publications (2024-2025) demonstrate expertise in fractional order PID controllers for time-delay systems, obesity-specific drug distribution models, and AI-enhanced pain monitoring during anesthesia. His work spans mathematical control theory, clinical implementation, and industrial applications. Scientific Recognition: Best Paper Award, IEEE Control System Society (2015) Erhan Yumuk contributes to Journal of Process Control , IFAC Journal of Systems and Control , and Applied Sciences , with emerging leadership in postdoctoral research at Ghent University.

Mohammad Qamarul IslamView profile

Professor

• Statistical Inference
• Robust Statistical Methods
• Nonparametric Methods
• +4 more

Prof. Mohammad Qamarul Islam is a Professor in the Statistics Department at Middle East Technical University (METU), Turkey, a position held since 2016. Previously, he served as Professor (2010-2016), Associate Professor (2005-2010), and Assistant Professor (1999-2005) in the Economics Department at Cankaya University. His academic career spans over four decades with prior appointments at METU (1983-1999) and Pakistani institutions including Gomal University and Karachi University. Education: Ph.D. in Statistics (1989), Middle East Technical University, Turkey M.Sc. in Statistics (1972), University of Karachi, Pakistan Research Focus: Prof. Islam specializes in advanced statistical methodologies with emphasis on Statistical Inference and Robust Methods . His work bridges theoretical statistics and practical applications through Nonparametric Techniques , Multivariate Analysis , and Econometric Modeling . Current investigations focus on regression under non-standard error distributions and experimental design optimization, contributing significantly to statistical theory validation in real-world scenarios. Publication Trends: His 2012-2016 publications reveal concentrated expertise in multivariate regression under non-ideal conditions (non-normal/elliptical errors), demonstrating methodological innovations for robust parameter estimation. Recent work extends to model uncertainty quantification in time series, reflecting his evolving focus on practical statistical challenges in economic and scientific data analysis. Academic Service: Information regarding student advising, research grants, laboratory affiliations, or scientific awards is not documented in the provided materials.

• Statistics
• Econometrics
• Panel Data Analysis
• +5 more

Assoc. Prof. Elif Tuna serves in the Department of Statistics at Yildiz Technical University's Faculty of Arts & Science, where she advanced from Research Assistant (2002) to Assistant Professor (2012) before promotion to Associate Professor. Her doctoral training in Econometrics at Marmara University established her interdisciplinary approach bridging statistical theory and socioeconomic applications. Her educational qualifications include: Doctorate in Econometrics, Marmara University (2005-2010) Postgraduate in Statistics, Yildiz Technical University (2002-2005) Undergraduate in Econometrics, Istanbul University (1997-2001) Tuna's research centers on entropy-based statistical methodologies, panel data modeling, and nonlinear econometric analysis. She pioneers applications in environmental economics (Environmental Kuznets Curve validation), happiness economics during crises, and market concentration metrics. Her work consistently integrates information theory with real-world socioeconomic phenomena, particularly migration patterns and pandemic impacts. Recent publications (2021-2024) reveal intensified focus on dynamic modeling of pandemic effects using Markov switching frameworks, while maintaining her foundational work on entropy applications. She demonstrates methodological versatility across time series, panel data, and categorical analysis, with geographic emphasis on Turkish socioeconomic contexts. Scientific Awards: No awards documented in source materials She has supervised 2 graduate theses and contributed to two major funded projects: a 2014-2016 basin-level water resource study and a 2005-2007 nationwide evaluation of Turkish statistics education. Her collaborative work often involves cross-disciplinary teams from economics and environmental science departments. No dedicated laboratory facilities or permanent research teams are specified, though her project involvement suggests participation in university-wide research consortia.

Orhan Deniz GENÇAĞAView profile

Assistant Professor

• Signal and Image Processing
• Machine Learning
• Knowledge Discovery and Data Mining
• +9 more

Orhan Deniz GENÇAĞA is an Assistant Professor in the Department of Electrical and Electronic Engineering at Antalya Bilim University since 2017. Prior to this, he held postdoctoral and research associate roles at SUNY Albany (2007), National Oceanic and Atmospheric Administration (NOAA) Center of CUNY (2009–2011), University of Texas Department of Space Sciences, and Carnegie Mellon University. His academic career spans interdisciplinary projects in signal processing, statistics, and systems modeling. Education: Ph.D. in Electrical and Electronic Engineering from Boğaziçi University M.S. in Electrical and Electronic Engineering from Boğaziçi University B.S. in Electrical and Electronic Engineering from Yıldız Teknik University Research Interests: GENÇAĞA’s work focuses on statistical signal processing, Bayesian inference, uncertainty modeling, causality, chaos theory, and remote sensing. His expertise extends to heavy-tailed processes, acoustic echo cancellation, source separation, and system identification, with applications in machine learning, information theory, and speech processing. Professional Roles and Affiliations: He chaired the IEEE Pittsburgh Signal Processing and Control Systems Societies, served as general chair for the First ECEA, and organized MaxEnt 2007. Additionally, he is a member of the editorial board of Entropy and an inventor in two US patents. Scientific Awards: NATO Research Fellowship

Serkan SaritasView profile

Assistant Professor

• Information Security
• Networked Control
• Communication Theory
• +3 more

Dr. Serkan Saritas is an Assistant Professor in the Department of Electrical and Electronics Engineering at Middle East Technical University (METU). He holds a Ph.D. from Bilkent University (2018) and completed postdoctoral research at KTH Royal Institute of Technology in Sweden (2018–2021). His research focuses on Networked Control Systems, Game Theory applications in security, Communication Theory, and Information Theory. He has authored numerous high-impact publications in top-tier venues such as IEEE Transactions on Automatic Control, IEEE Transactions on Information Theory, and Automatica. Education: Ph.D. in Electrical and Electronics Engineering, Bilkent University (2013–2018) M.Sc. in Computer Engineering, Bilkent University (2010–2013) B.Sc. in Electrical and Electronics Engineering, Bilkent University (2005–2010) Research Interests: Game-Theoretic Security (e.g., adversarial attacks, authentication strategies) Networked Control Systems (resilience, fault tolerance) Information Theory (signaling games, estimation theory) Communication Theory (channel modeling, detection algorithms) Awards: Recipient of the IEEE Turkey PhD Thesis Award (2020), TÜBİTAK scholarships for graduate studies, and top national exam rankings (1st in ALES 2014, 2nd in ÖSS 2005). Teaching: Recently taught courses like EE348 (Logic Design) and EE494 (Engineering Design) at METU.

FESİH KESKİNView profile

Assistant Professor

• Signal Processing
• Antenna Array Design
• Direction of Arrival Estimation
• +5 more

Asst. Prof. Dr. FESİH KESKİN is affiliated with the Faculty of Engineering at Iğdır University , where he holds a position in the Department of Electrical and Electronics Engineering . His research focuses on signal processing, antenna array design, and robotic systems integration with telecommunications. He earned his PhD from Eskişehir Technical University (2014-2021), MSc from Anadolu University (2011-2014), and BSc from Fırat University (2005-2009). Key research areas include direction of arrival (DOA) estimation , 3D antenna array configurations , and machine learning applications in agriculture and energy systems. His work on volumetric array designs addresses challenges in azimuth/elevation angle estimation under array imperfections. Recent studies also explore Industry 4.0 automation and solar energy storage solutions. Notable contributions include: Development of UAV swarm-based 3D antenna arrays for isotropic DOA estimation XGBoost-driven frameworks for driving cycle generation Integration of deep learning architectures for fruit detection systems No awards or patents are listed in his profile. He maintains active collaboration networks in telecommunications and robotics domains.

Ramazan ÇağlarView profile

Associate Professor

• Electric Power Distribution
• Reliability Assessment
• System Reliability
• +11 more

Ramazan Çağlar is an Associate Professor in the Department of Electrical Engineering at Istanbul Technical University, College of Engineering. His research focuses on modern power systems, including microgrids, renewable energy integration, reliability assessment, and intelligent control of distributed energy resources. He actively publishes in high-impact journals and leads research initiatives in smart grid technologies. Research Interests: His work spans electric power distribution, system reliability, microgrid dynamics, induction motors, and power transmission. He integrates advanced computational methods such as machine learning, Bayesian inference, and optimization algorithms to solve complex problems in energy systems. His recent focus includes fault prediction using drones, energy forecasting with neural networks, and optimal allocation of distributed generators. Recent Research Trends: Analysis of his latest publications (2022–2024) reveals a strong trend toward data-driven and AI-enhanced modeling in microgrids and renewable integration. He combines physical models with machine learning (e.g., neural ODEs, autoencoders, LSTMs) and applies multi-objective optimization techniques like multiverse optimization. His work increasingly emphasizes uncertainty quantification, real-time control, and sustainable energy solutions. Scientific Projects: Completed: Reliability Evaluation of Power Station Designed for DC-Fed Traction Systems in Light Rail Transit (2011–2021). Advising and Grants: He is currently supervising 14 theses in progress, indicating an active role in mentoring graduate students. While specific grant details are limited, his long-running project funded by ITU's BAP (Scientific Research Projects) suggests sustained research funding. His collaborations include international researchers, particularly in Africa and the Middle East. Labs and Research Teams: Though not explicitly named, his research activities suggest leadership in a power systems and smart grid laboratory at ITU, focusing on reliability, optimization, and AI applications in energy infrastructure.

Guvenc ArslanView profile

Professor

• Statistics
• Machine Learning
• Data Mining
• +4 more

Guvenc Arslan serves as a Professor in the Department of Statistics within the Faculty of Engineering and Natural Sciences, focusing on advanced statistical methodologies and machine learning applications. His work bridges theoretical statistics with real-world problem solving across diverse domains. His core research interests include: Machine Learning and Classification Algorithms Statistical Clustering Methods Applied Statistics in Healthcare and Earth Sciences Data Mining and Pattern Recognition Fuzzy Logic and Bayesian Methods Parameter Estimation and Distribution Theory Analysis of his 15 most recent publications (2024-2015) reveals consistent innovation in classification techniques, particularly for medical diagnostics (e.g., COVID-19, cryotherapy) and environmental monitoring (e.g., earthquake engineering, satellite data analysis). His methodology frequently integrates support vector machines, k-means clustering, and fuzzy Bayesian approaches to address complex data challenges. Dr. Arslan's scholarly contributions extend to software development, including a JAVA implementation for multivariate statistical testing, demonstrating his commitment to practical tool creation alongside theoretical advancement. His research maintains strong connections to both medical applications and geospatial engineering problems.

• Spatial Point Processes
• Stochastic Geometry
• Bayesian Inference
• +3 more

Jesper Møller is a Professor in the Department of Mathematical Sciences at Aalborg University's Faculty of Engineering and Science, specializing in Statistics and Mathematical Economics. His research spans advanced stochastic modeling and spatial statistics. Key affiliations: Danish National Research Foundation (MaPhySto), Independent Research Fund Denmark, Centre for Stochastic Geometry and advanced Bioimaging Research Interests His work focuses on spatial point processes, including Cox processes, determinantal point processes, and Matern hard-core models. He develops computational methods like MCMC, Bayesian inference, and likelihood-based techniques for spatio-temporal data, with applications in forest fire modeling, bioimaging, and mobile service scenarios. Scientific Contributions Projects include: Generalized shot noise Cox processes Statistical inference for log Gaussian Cox processes Transforming point processes into Poisson processes Stochastic geometry for germ-grain models Collaborations He has collaborated with researchers across Denmark, Norway, Australia, Canada, and the USA, working on topics ranging from Hawkes processes to Voronoi tessellations.

Eric ThraneView profile

Professor

• Gravitational Waves
• Astrophysics
• Cosmology
• +5 more

Eric Thrane is a Professor at Monash University's School of Physics and Astronomy, specializing in gravitational wave astronomy, astrophysics, and cosmology. His work combines data from gravitational wave observatories like LIGO with electromagnetic telescope observations to study compact binaries, neutron stars, and black holes. Institution: Monash University (School of Physics and Astronomy) Research Themes: Gravitational waves, cosmology, Bayesian inference, dark matter detection Thrane's recent research focuses on gravitational wave transient analysis, including GW231123 , black hole mass-spin correlations, and nanohertz gravitational wave detection via pulsar timing arrays. He develops advanced Bayesian frameworks like GammaBayes for dark matter searches and Bilby for gravitational wave inference. His group publishes extensively in journals like Physical Review D , Monthly Notices of the RAS , and Nature Astronomy . Collaborations span LIGO, Virgo, OzGrav, and the Cherenkov Telescope Array. Thrane leads major grants including a $460K ARC Discovery Project and contributes to $37M OzGrav 2 center. Awards: Special Breakthrough Prize in Fundamental Physics (2016), ARC Future Fellowship (2015) Outreach: Featured in ABC National Breakfast, Catalyst, and The Conversation