جست‌وجوی استاد و پژوهشگر

Eric Thraneمشاهده پروفایل

استاد

• Gravitational Waves
• Astrophysics
• Cosmology
• +۵ مورد دیگر

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

• Stochastic inventory models
• Stochastic maintenance models
• Bayesian applications to inventory
• +۵ مورد دیگر

Prof. Dr. Ülkü Gürler is a Professor at Bilkent University with contactable office (EA314), email (ulku@bilkent.edu.tr), and telephone (+90 (312) 290 1520) details indicating active academic status. Her educational credentials include: Ph.D. in Statistics from the Wharton School, University of Pennsylvania (1990) M.S. in Statistics from the University of Pennsylvania (1987) B.S. in Applied Statistics from Middle East Technical University (1979) Prof. Gürler's research spans theoretical and applied statistics with emphasis on operational modeling. Her work integrates stochastic processes with real-world inventory and maintenance systems, while advancing methodological approaches for censored/truncated data through Bayesian and nonparametric frameworks. This creates synergies between reliability engineering and survival analysis methodologies. Her scholarly contributions focus on developing robust statistical solutions for complex industrial and biomedical data challenges, particularly where conventional parametric methods fail due to data limitations.

Merve Şahin Kürşadمشاهده پروفایل

استادیار

• Measurement and Evaluation in Education
• Computer Adaptive Testing
• Test Equating
• +۷ مورد دیگر

Merve Şahin Kürşad is an Assistant Professor at TED University, Faculty of Education, Department of Educational Sciences. She actively teaches courses on Measurement and Evaluation in Education, Research Methods in Education, and 21st Century Skills for Teachers. Her work spans both undergraduate and graduate programs, including non-thesis master's programs in Educational Institutions Management. 2023-2025: Teaching Measurement and Evaluation in Education (ED 301), Research Methods in Education (ED 302, EDUC 204), and 21st Century Skills for Teachers (ED 350). 2023-2024: Advising Graduation Project (MEI 590) for the Non-Thesis Master's Program. Her research focuses on psychometrics, computerized adaptive testing, and educational assessment. Recent publications analyze item parameter drift, test equating, and metacognitive awareness in education. Her scholarly contributions include empirical studies on missing data techniques, validity/reliability frameworks, and adaptive testing efficiency. These works intersect with quantitative research methodology and educational technology. For communication, she uses merve.kursad@tedu.edu.tr and is based in Room F121 at TED University.

• Simulation Optimization
• Industrial Engineering
• Operations Research
• +۵ مورد دیگر

Mevlüde Ebrü Angün is an Associate Professor at Galatasaray University's Faculty of Engineering and Technology, Department of Industrial Engineering. Her research focuses on simulation optimization, industrial engineering, operations research, and disaster management. Doctorate in Operations Research, Katholieke Universiteit Brabant, Netherlands (2004) Postgraduate in Industrial Engineering, Boğaziçi University, Turkey (1998) Undergraduate in Industrial Engineering, Istanbul Technical University, Turkey (1995) Her research interests center on simulation optimization, stochastic programming, and disaster management. She applies these to retail operations, unmanned aerial vehicle (UAV) routing, and humanitarian logistics. Her work combines mathematical programming with practical applications in Turkey and international contexts. Scientific contributions include projects on UAV routing in disaster scenarios, retail assortment planning, and internal audit scheduling. She has supervised postgraduate theses on regression clustering and inventory optimization. Stochastic dual dynamic programming for disaster management Mixed-integer linear programming (MILP) for retail and auditing Risk-averse optimization under uncertainty She has served as a TUBITAK grant reviewer and peer reviewer for journals like Engineering with Computers and European Journal of Operational Research .

• Econometrics
• Statistics
• Time Series Analysis
• +۷ مورد دیگر

Özge GÜNDOĞDU is a Lecturer at Süleyman Demirel University in the Department of Econometrics under the Faculty of Economics and Administrative Sciences. She also holds an affiliation with the Department of Statistics. Bachelor’s in Business Administration, Anadolu University (2013) Bachelor’s in Statistics, Ondokuz Mayıs University (2009) Bachelor’s in Management Information Systems, Anadolu University (2024) Master’s in Econometrics, Süleyman Demirel University (2011) Doctorate in Statistics, Ondokuz Mayıs University (2016) Her research focuses on econometrics, time series analysis, and machine learning applications in financial economics. She has developed innovative neural network models for forecasting economic and financial trends, such as stock prices and regional economic indicators. Recent publications highlight her work on Gaussian activation functions in artificial neural networks, robust regression for human development indices, and ensemble learning methods for stock exchange forecasting. She also explores LSTM models for evaluating football club share prices and nonparametric statistics in econometric modeling. She teaches courses including Time Series Analysis, Multivariate Statistical Analysis, Advanced Econometrics, and seminars on econometric models.

• Software Testing
• Model-Based Testing
• Software Product Lines
• +۷ مورد دیگر

Assoc. Prof. Tugkan Tuglular is affiliated with the Department of Computer Engineering at Izmir Institute of Technology, leading the Safety, Security and Reliability of Software Systems (S2RS2) Laboratory. His research focuses on software testing methodologies, model-based systems, and security applications across software product lines. His work spans formal methods for testing (e.g., event sequence graphs, feature modeling), blockchain-enhanced data sharing, and empirical software engineering. He has extensively studied automated test case generation, change impact analysis, and robustness measurement using Bayesian networks. Notable contributions include domain-specific languages for business applications and frameworks for incremental testing in evolving systems. Recent efforts explore federated platforms for source code quality analysis and AI-driven methods for completing graphical software models. His research integrates theoretical foundations with practical tools, emphasizing security and reliability across diverse software domains. Awards: No specific awards listed. Advising/Grants: No student advising records or grant details provided in the text. His lab (S2RS2) focuses on advancing software safety and security through rigorous testing and modeling approaches. Labs/Teams: Leads the S2RS2 Lab, which investigates software systems' reliability through advanced testing techniques and formal modeling.

• Robust Statistics
• Statistical Modeling
• Multivariate Analysis
• +۵ مورد دیگر

Prof. Olçay Arslan is a Professor of Statistics at the Department of Statistics, Faculty of Science, Ankara University. He holds a Doctorate from the University of Leeds (1993) and has held academic positions including Associate Professor at Saint Cloud State University (2001-2003). His research focuses on robust statistical methods, multivariate analysis, and distribution theory with emphasis on heavy-tailed and skewed distributions. He has supervised 23 theses and published over 170 articles in peer-reviewed journals like Journal of Multivariate Analysis and Computational Statistics & Data Analysis . Education: PhD: University of Leeds, UK (1993) MSc: Ankara University (1986) BSc: Ankara University (1983) Key Roles: Head of Department (2014-2023) Member of University Academic Incentive Evaluation Commission (2016-present) Editorial Board member for multiple journals Research Interests: His work centers on robust statistical methodologies addressing outliers and non-normality in data. Notable areas include: Robust estimation techniques Mixture models and clustering Heavy-tailed distributions Regression analysis with autoregressive errors Computational statistics Recent Contributions: Recent studies emphasize: Variable selection in joint mean-covariance models Nonparametric tests for serial independence Robust methods for asymmetric/heteroscedastic data Applications of skew Laplace distributions Grants & Teams: Lead researcher on 4 funded projects. Active member of the Robust Statistics Research Group at Ankara University, collaborating on multidisciplinary data science initiatives.

• Blind Signal Processing
• Bayesian Estimation
• Particle Filters
• +۳ مورد دیگر

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.

Ayşen Dener Akkayaمشاهده پروفایل

استاد

• Time series analysis
• Stochastic processes
• Risk and decision analysis
• +۳ مورد دیگر

Prof. Ayşen Dener Akkaya is a Professor in the Department of Statistics at Middle East Technical University (METU), where she has held academic positions since 1985. She earned her Ph.D. in Statistics from METU in 1995, following her M.Sc. (1987) and B.Sc. (1985) in the same discipline. Her professional experience includes a Visiting Fellowship at Princeton University's Department of Civil Engineering and Operations Research (1997–1998). Her research focuses on time series analysis , stochastic processes , and risk analysis , with applications in earthquake engineering, seismic hazard modeling, and robust statistical methods. Key areas include autoregressive models, parameter estimation under non-normal conditions, and stochastic modeling of geotechnical systems. Her publications span journals like Journal of Computational and Applied Mathematics and Structural Safety , emphasizing robust statistical techniques and their engineering applications. She co-authored the influential textbook Robust Estimation and Hypothesis Testing (2004), which remains a key reference in statistical methodology.

• Data Science & Analytics
• Machine Learning
• Computational Statistics
• +۵ مورد دیگر

Professor İnci Batmaz, affiliated with the Department of Statistics at Middle East Technical University (METU), has held her current academic rank since 2012. She is also an affiliated faculty member of METU's Institute of Applied Mathematics (Financial Mathematics Program) and has served in administrative roles such as Chair of the Department of Statistics and Administrative Coordinator for institutional development initiatives. Ph.D. in Computer Engineering, Ege University (1993) Ph.D. Dissertation at Carnegie-Mellon University (Fulbright Scholar, 1988-1991) M.S. in Computer Engineering, Ege University (1986) B.S. in Statistics, METU (1984) Her research spans Data Science, Machine Learning, and Computational Statistics with applications in Environmental Statistics, Quality Improvement, and Financial Analytics. Key methodologies include Response Surface Methodology, Design of Experiments, and advanced regression splines (MARS, CMARS, RCMARS). She has made significant contributions to statistical modeling of climate patterns, financial systems, and industrial quality control. Recent publications focus on regression splines for predictive modeling (2021), climate change analysis (2016), robust statistical methods under uncertainty (2011), and machine learning applications in finance and environmental science. Her work emphasizes both theoretical developments and real-world implementations across diverse domains. Scientific Awards TÜBİTAK International Post-Doctoral Fellowship (2016-2017) Multiple METU Faculty Performance Awards (2003-2013) Best MSc Thesis Awards (2009, 2012) as co-advisor Fulbright Doctoral Scholarship (1988-1991) Seven TÜBİTAK research grants and publication supports She has advised two MSc students to award-winning theses and collaborated with international researchers including Prof. Gerhard W. Weber. Her professional experience includes visiting positions at Imperial College London and Tilburg University, along with board memberships in academic institutions like the Graduate School of Natural and Applied Sciences.

Shervin Rahimzadeh Arashlooمشاهده پروفایل

استادیار

• Machine Learning
• Computer Vision
• Kernel Methods
• +۴ مورد دیگر

Shervin Rahimzadeh Arashloo is an Assistant Professor in the Department of Computer Engineering at Bilkent University, Faculty of Engineering. His research focuses on machine learning, kernel methods, anomaly detection, and computer vision applications in fields like face recognition and environmental modeling. He holds a Ph.D. and has extensive academic publications since 2007. His work integrates advanced statistical techniques with real-world challenges, such as hydrological drought modeling, sediment transport prediction, and face spoofing detection. Key contributions include novel one-class classification frameworks, kernel fusion methodologies, and multi-output regression models for environmental systems. Research trends in his publications emphasize robust anomaly detection via kernel-based approaches, optimization of classifier combinations, and application of machine learning to geospatial and biometric security problems. No awards are explicitly mentioned in the provided materials. His lab likely focuses on interdisciplinary projects combining computer science with environmental engineering, though specific lab/team details are not elaborated here.

Özlem TÜRKER BAYRAKمشاهده پروفایل

دانشیار

• Statistics
• Time Series Analysis
• Quality Control
• +۵ مورد دیگر

Dr. Özlem TÜRKER BAYRAK is an Associate Professor at Çankaya Üniversitesi, primarily affiliated with the Department of Statistics under the Inter-Curricular Courses unit. She has held academic roles since 1996, including Assistant Professor at the Faculty of Engineering and Visiting Position at University of Waterloo. Her research focuses on statistical methodologies, time series analysis, quality control, environmental acoustics, and energy economics. She has advised 11 graduate students and contributed to projects like the Turkish soundscape evaluation and robust estimation techniques. She teaches courses such as Engineering Statistics and Systems Analysis. Key research areas include non-normal distribution modeling, soundscape perception, and energy market dynamics. Recent work includes studies on urban nightlife impacts and adaptive statistical inference. She has over 40 publications in journals like Applied Acoustics and Quality and Reliability Engineering International.

Cansu ÇİLİNGİR KARAمشاهده پروفایل

عضو هیئت علمی

• Financial Performance Analysis
• Corporate Governance
• Stock Market Dynamics
• +۷ مورد دیگر

Cansu ÇİLİNGİR KARA is a Research Assistant in the Department of Finance and Banking at Beykent University, Faculty of Economics and Administrative Sciences. She previously served as a Research Assistant at Istanbul Aydın University from 2020 to 2022 in the Department of Accounting and Finance Management. She holds a Ph.D. from Marmara University's Institute of Social Sciences, completed in 2019. Education: Doctorate (2019), Institute of Social Sciences, Marmara University — Thesis: 'The effect of financial slack on firm performance and stock' Master's Degree (2017), Institute of Social Sciences, Marmara University — Thesis: 'Financial performance evaluation with TOPSIS method and its relationship with stock returns' Licence (2013), Faculty of Economics and Administrative Sciences, Istanbul Aydın University Her research focuses on financial performance analysis, corporate governance, stock market dynamics, and econometric modeling. She employs advanced statistical and multi-criteria decision-making methods such as Toda-Yamamoto causality, Granger causality, MOORA, and TOPSIS to analyze firm performance and market behavior. Her work bridges theoretical finance with empirical applications in Turkish and international markets. The recent articles (2020–2024) reflect a consistent focus on financial performance, causality in investment instruments, and governance structures. Her work spans both national and international journals, demonstrating expertise in empirical finance and quantitative analysis. She frequently investigates relationships between macroeconomic indicators and financial markets using robust econometric techniques. Scientific Awards: No awards mentioned in the provided text. She has advised no listed students but has been involved in academic service through administrative duties. These include serving as Exam Coordinator and as a member of the Organizing Committee for the 1st and 2nd Congress of Economics and Finance and the Türkiye Migration Conference at Beykent University. She has not received mention of research grants in the data provided. Labs, Teams, and Collaborative Work: Collaborated with researchers such as Yılmaz Türkmen Sibel, Korkmaz Selin, Alpay Serdar, and Aydın Ruhan. Active participant in academic conferences and symposia, indicating integration into research networks in finance and economics.

• Statistics
• Applied Statistics
• Operations Research
• +۵ مورد دیگر

Dr. Elmas Burcu Mamak Ekinci is a Lecturer in the Computer Engineering Department at Başkent University. Holding a PhD in Statistics (2011) from Ankara University, she has 15+ years of academic experience. PhD (2011), Ankara University Master's (2005), Ankara University Bachelor's (2001), Ankara University Her research focuses on statistical analysis applied to ergonomics , emergency medicine , digital transformation , and consumer behavior . Recent work includes multi-criteria decision-making approaches for evaluating physical workload and digital performance metrics. Publications span journals like Pain Medicine , Ergonomics , and Journal of Supercomputing , with a current H-index of 2. Her methodological expertise includes fuzzy logic , nonparametric statistics , and data-driven decision frameworks . She has served as a referee for IJOCTA (2024) and contributed to textbook chapters on fuzzy EDAS methods and biostatistical principles .

• Optimization
• Multi-criteria Decision Making
• Production Planning
• +۲ مورد دیگر

Selin Özpeynirci is a Professor in the Department of Industrial Engineering at the Faculty of Engineering, İzmir University of Economics, where she has been employed since February 2008. Her academic career began as a research assistant at Middle East Technical University's Department of Industrial Engineering from 2002 to 2007, where she also completed all her degrees. She has held visiting researcher positions at prestigious French institutions including Ecole Centrale Paris (2014-2015) and Centrale Supelec (2019-2020), supported by TÜBİTAK 2219 grants. Her educational background includes: B.S. in Industrial Engineering, Middle East Technical University (2002) M.S. in Industrial Engineering, Middle East Technical University (2004) Ph.D. in Industrial Engineering, Middle East Technical University (2007) Özpeynirci's research focuses on optimization methodologies applied to complex industrial systems. She specializes in multi-criteria decision making frameworks that address real-world constraints in production environments. Her work bridges theoretical advances in mathematical programming with practical applications in manufacturing scheduling and resource allocation. Current investigations emphasize interactive algorithms that incorporate human decision-maker preferences into computational models, particularly for sorting and selection problems with equity considerations. Analysis of her 15 most recent publications reveals a strong trajectory in inverse multiple criteria problems and constrained resource allocation. Her work consistently integrates flexible manufacturing systems concepts with advanced decision theory, showing increasing sophistication in handling preference uncertainty and balance requirements. The publications demonstrate methodological evolution from classical scheduling approaches toward hybrid interactive-optimization frameworks that accommodate human judgment in complex decision environments. Her scientific recognition includes: TÜBİTAK 2219 Research Grant for Ecole Centrale Paris (2014-2015) TÜBİTAK 2219 Research Grant for Centrale Supelec (2019-2020) Özpeynirci actively collaborates with international researchers including Vincent Mousseau (France), Özgür Özpeynirci (Turkey), and Burak Gokgur (Turkey) on projects spanning multiple criteria decision analysis and production systems optimization. Her current research agenda explores drone-integrated logistics and advanced decomposition methods for large-scale inverse sorting problems, supported by ongoing institutional affiliations and research grants. While specific lab information isn't provided in the source material, her work clearly operates within the industrial engineering research ecosystem focused on computational decision support systems.