Research Group
Our research group focuses on the development and application of modern statistical and computational methods for complex data-driven problems. Our research interests span statistical learning, machine learning, fuzzy systems, forecasting, ensemble learning, metaheuristic optimization, and computational statistics.
A particular emphasis is placed on developing novel methodological frameworks that combine statistical modelling with computational intelligence, including Meta-Fuzzy Functions (MFF), Type-1 Fuzzy Functions (T1FF), ensemble and hybrid learning approaches, and nature-inspired optimization algorithms.
The group brings together researchers from statistics, biostatistics, machine learning, and related interdisciplinary fields and collaborates on methodological as well as applied research projects.
Principal Investigator
Nihat Tak
Associate Professor of Statistics
Research interests include statistical learning, fuzzy systems, machine learning, forecasting, ensemble methods, metaheuristic optimization, and computational statistics.
Research themes:
Meta-Fuzzy Functions (MFF) · Type-1 Fuzzy Functions · Forecasting · Ensemble Learning · Machine Learning · Metaheuristic Optimization
Faculty Members
Ayşegül Yabacı Tak
Associate Professor of Biostatistics
Research interests include biostatistics, statistical modelling, machine learning, biomedical data analysis, and interdisciplinary applications of statistical methods.
Erdinç Karakullukçu
Assistant Professor of Computer Sciences
Research interests include computational statistics, machine learning, optimization algorithms, fuzzy systems, and data-driven modelling.
Aylin Uçan
PhD
Research interests include statistical modelling, machine learning, computational methods, and applied data analysis.
Graduate Researchers
Ramazan Akman
PhD Student
Research interests include statistics, machine learning, computational modelling, and data analysis.
Burhan Kılıçkaya
MSc Student
Research interests include statistical methods, machine learning, optimization, and computational data analysis.
Sadık Çoban
BSc Student
Research interests include statistical computing, R programming, machine learning, and the development and implementation of statistical software.
Current Research Directions
Our current research activities are particularly concentrated on:
- Meta-Fuzzy Functions (MFF) and new approaches to model combination
- Type-1 Fuzzy Functions (T1FF) for regression and forecasting
- Machine-learning ensembles and hybrid predictive systems
- Metaheuristic optimization and algorithm selection
- Feature selection and high-dimensional modelling
- Statistical and machine-learning methods for biomedical data
- R package and statistical software development