ECE285 Agentic AI and LLM for Smart Grids [Course Website]
Description: This new interdisciplinary graduate course bridges power systems and modern AI and data science. Students will learn both the fundamentals of power systems, as well as advanced AI techniques, with a focus in the Winter 2026 offering on how Agentic AI and Large Language Models (LLMs) can transform the operation, optimization, and control of future smart grids.
ECE228 Machine Learning for Physical Applications [Course Website]
Description: This course provides an introduction to physics-guided deep learning and its applications in physical systems and control. The course includes both the practical and theoretical aspects of the following topics: linear regression and classification, LASSO and ridge regressions, feedforward neural networks, recurrent neural networks, Transformers, physics-informed neural networks, neural operators, Neural ODEs, incorporating optimization layers into learning, Gaussian processes and uncertainty quantification. It involves a group-based course project that provides a hands-on opportunity in conducting machine learning projects for physical applications!
ECE171B Linear Control System Theory [Course Website]
Description: This course provides an introduction to linear system control theory with applications in physical, biological, and engineering systems. t focuses on the fundamental principles of linear feedback control design in state space. Both practical and theoretical aspects are covered, including: review of ODEs and linear algebra; system modeling; equilibrium points and linearization; stability of linear time-invariant (LTI) systems; reachability, controllability, and stabilizability of LTI systems; state feedback controller design; observability and detectability; observer and output feedback design; linear quadratic regulator (LQR); Kalman filtering; and an introduction to Lyapunov stability analysis and reinforcement learning, with emphasis on their applications in linear control systems.