Currently a Master's Data Science student at the University of Amsterdam, Rezi's background is rooted in Physics. During his undergraduate years at NYU Abu Dhabi, he developed an interest in programming and data science as a way to translate physical theories into functional models. His trajectory took a significant turn at the National Center for Disease Control (NCDC); engaging with large-scale health data redirected his focus toward biological complexity and the systemic challenges of population health. Now, for his thesis, Rezi is building a metabolic model of Type 2 Diabetes progression that integrates mechanistic ODEs with Scientific Machine Learning (SciML). Specifically, he uses Deep Operator Networks (DeepONets) to map how chronic stress drives disease trajectories within the UK Biobank. His goal is to develop a framework that scales to thousands of individuals without losing the biological specificity that makes the model clinically meaningful.