As a visiting PhD researcher at the Process Intelligence Research group, my exchange project focuses on developing machine learning-augmented optimization frameworks for computational strain design. My Ph.D. research aims to integrate Metabolic Engineering and Process Systems Engineering by identifying economically optimal bioprocess configurations for genetically engineered microorganisms. I combine synthetic data, machine learning, and evolutionary optimization to uncover hidden design synergies and improve optimization-driven decision-making. I am particularly interested in developing hybrid modeling frameworks that improve model fidelity while maintaining computational tractability, ultimately enabling holistic optimization across the entire bioprocess.