Dr. Soykan's research integrates mathematical optimization, machine learning, and optimal control to develop data-driven computational tools for decision-making in dynamic and uncertain environments. He focuses on creating adaptive models that leverage real-time data to improve efficiency, robustness, and predictive capabilities. By combining theory with practical applications, his work advances solutions for complex challenges in areas such as digital twins, and autonomous systems.