Data Science & Engineering research is supported by premier faculty and advanced computational laboratories focused on extracting insight from data and enabling intelligent decision-making in complex engineered systems. These efforts are driven by the need for scalable, data-driven, and uncertainty-aware solutions across sectors including energy systems, manufacturing, healthcare, and infrastructure. The work emphasizes data-centric modeling, predictive analytics, and computational decision-making under uncertainty. Faculty integrate statistical learning, probabilistic reasoning, and optimization methods to develop robust and interpretable solutions for real-world engineering problems.
Collectively, these efforts enable improved prediction accuracy, better decision quality under uncertainty, and enhanced system performance through integrated analytics, simulation, and optimization frameworks.
Data Analytics & Modeling
Probabilistic Modeling
Simulation
Optimization