I am a chemical engineer, and I am using mathematical modeling and machine learning to enhance biological and chemical process predictions. During my academic career at KU Leuven (postdoc) and the Process Systems Engineering Department of Imperial College of London (Ph.D.), I gained knowledge in mathematical modeling, simulations, systems validation, data analysis, and process optimization. I therefore focus on creating mathematical models to simulate processes, implement control, scale up, and use machine learning. I constantly solve difficult scientific and engineering problems with novel, real-world solutions. I design theoretically and empirically valid predictive frameworks using my knowledge of mechanistic modeling and data-driven approaches. This combined knowledge has enabled disease dynamics modeling, crystallization simulation, renewable energy optimization, and water purification technology improvements. My achievements include growing peer-reviewed publications in high-impact journals, Ph.D. student mentoring in various fields, and frequent attendance at international conferences like AIChE and ASME. Python, MATLAB, COMSOL, and OpenFOAM are my tools for algorithm development, data analysis, simulations and high-fidelity modeling. I have delivered graduate-level instruction in differential equations, machine learning, and reactor design. I seek to advance impactful research and education in several fields through international collaboration and innovation.