About Me

I am an Assistant Professor in the Department of Actuarial Science, Faculty of Science, at Selçuk University. Building on the analytical foundation of my PhD in Statistics, I conduct research on data-driven problems by combining statistical methods with artificial intelligence, machine learning, and deep learning.

My academic work focuses not only on developing models that generate predictions, but also on understanding how reliable these models are, under what conditions they may fail, and to what extent their outputs can be trusted.

Research and Academic Work

My research covers various application areas of artificial intelligence and machine learning. In particular, I work on trustworthy AI, model reliability, uncertainty quantification, conformal prediction, and failure detection.

In medical AI and biomedical image analysis, I investigate deep learning-based classification and segmentation models from the perspectives of reliability, distribution shifts, failure analysis, and model safety. I also conduct research in areas such as multimodal learning, large language models, retrieval-augmented generation (RAG), machine unlearning, and industrial anomaly detection.

My research emphasizes empirical evaluation, statistical analysis, comparative methods, and reproducible research practices. My primary goal is to contribute to the development of AI systems that not only achieve high performance but can also be scientifically evaluated in terms of their limitations and reliability conditions.

Education and Technology

Alongside my research, I teach courses in programming, data science, data mining, blockchain technologies, and financial technologies.

My teaching approach emphasizes combining theoretical knowledge with practical application. I aim to help students develop skills not only in using existing tools, but also in acquiring and processing data, selecting appropriate methods, developing software, evaluating experimental results, and presenting their findings within a scientific framework.

By exploring tools such as Python, machine learning, and data analysis through real-world datasets and practical problems, I support students in developing their research and project development skills.

Academic Approach

I believe scientific progress depends not only on proposing new methods but also on critically testing them and evaluating their results transparently.

Accordingly, my research focuses on reliability as well as performance, failure conditions as well as average performance, and the limits within which model outputs can be interpreted, alongside the results themselves.

Through this website, you can access my academic publications, research interests, course syllabi, weekly teaching schedule, and latest announcements.