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 manufacturing, healthcare, defense, space, cyber-physical systems, wireless communication systems, and energy.
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 improve prediction accuracy, decision quality under
uncertainty, and system performance through integrated analytics, simulation, and
optimization frameworks.
Artificial Intelligence & Machine Learning
Deep Learning, Federated and Distributed learning, Optimization for Machine
Learning, Generative AI, Graph Neural Networks, Large Language Models and Agentic AI.
Data Analytics & Modeling under Uncertainty
Statistical Learning, Predictive Modeling, Uncertainty Quantification, Calibration, Statistical Process & Quality Control, Bayesian Optimization, and Smart Manufacturing.
Intelligent Decision-Making
Reinforcement Learning, Autonomous Systems, AI for Optimization, Algorithms.
Digital Twins
System Modeling, Stochastic Analysis, Risk Analysis, Physics-Informed Machine Learning, Real-Time Decision-Making.
