Teaching

Introduction to intelligent engineering decision support systems (iDSS) for normative and descriptive approaches in decision analysis. Concepts include theories of decision-making, multi-objective analysis and optimization, intelligent representations of human behavior - AI decision surrogates – expert systems – case based reasoning, and decision-making under risk and uncertainty.

Integrates network-based models and deep learning to solve complex systems engineering problems. Analyzes system architecture through graph metrics, motifs, and probabilistic models. Creates predictive frameworks using Artificial Neural Networks (ANN) and Graph Neural Networks (GNN) to support the design and analysis of large-scale complex systems.