Assist. Prof. Dr. Samira Rahim | Computational Finance | Best Researcher Award

Samira Rahim specializes in the field of stochastic methods, statistical modeling, and time series analysis, with a strong focus on the development and application of advanced probabilistic techniques for real-world data interpretation. Her research explores complex stochastic models, particularly in financial and meteorological contexts, where uncertainty and periodicity play critical roles. Through her work on periodic GARCH and threshold GARCH models, Rahim has contributed to improving the understanding of volatility dynamics and the statistical behavior of financial series with cyclical characteristics. Her investigations emphasize the importance of model mixtures and the application of local asymptotic normality principles, offering deeper insights into how stochastic processes can better capture temporal dependencies in financial and environmental data. With a background in statistics and operational research, Rahim integrates both theoretical and applied perspectives to construct robust predictive and inferential frameworks. Her expertise extends to stochastic optimization and probabilistic analysis, contributing to the development of more adaptive forecasting tools for risk management and decision-making under uncertainty. Her academic contributions, including her publication in Communications in Statistics – Theory and Methods in 2025, demonstrate her commitment to advancing research in the mathematical and statistical sciences. Rahim’s long-term experience in academia and her engagement with research projects underscore her dedication to innovation in stochastic modeling and data-driven approaches. Her work aligns with contemporary challenges in data science, emphasizing the critical role of statistical methodologies in improving predictive accuracy and understanding complex systems governed by random processes. Samira Rahim’s research continues to strengthen the intersection of probability theory, operational research, and applied statistics, contributing significantly to both theoretical advancements and their practical implementations in diverse scientific domains.