Introduction

The Electrical and Computer Engineering Ph.D. in Intelligent System, Robotics and Control (ISRC) equips students for a research career on the design of intelligent systems that can sense the world, reason about and make decisions on the world, perhaps all in real-time. The electric and power system, that interconnects billions of users and physical devices, is a representative system and application in this discipline.

The development of sophisticated systems is necessarily an interdisciplinary activity. To sense and succinctly represent events in the world requires knowledge of signal processing, information theory, and data analysis; to make good decisions about highly complex systems requires knowledge of mathematical optimization theory and domain knowledge in power system modeling and dynamics; and to act upon the world requires familiarity with concepts of control theory and machine learning. In addition to the theoretical optimization, control and machine learning aspects, many important hardware and software issues must be addressed in order to obtain an effective fusion of a complicated suite of sensors, computers, and problem dynamics into one integrated system - to enable the future smart grid.

All students in ISRC must satisfy the requirements of the ECE Doctoral Program as described on the ECE website. The following core and elective courses are designed to support the organization of the Department Qualifying Exam (DQE), which is ultimately up to the student’s committee.

Core

All students will take the core courses.

ECE 269 Linear Algebra & Applications 

ECE 271A Statistical Learning I

ECE 272A Stochastic Processes in Dynamical Systems

ECE 276A Sensing & Estimation

Electives

• Recommended Power and Energy System graduate courses: 

  • Fundamentals of Energy Systems (MAE 206)

  • Electric Power Systems Modeling (MAE 243)

  • Machine Learning for Physical Applications/Power Systems (ECE 228)

  • Convex Optimization & Applications (ECE 273)

• 3 courses from the following list: ECE 250 Random Processes; ECE 252A-B Speech Compression, Speech Recognition; ECE 271B-C Statistical Learning II, Deep Learning & Applications; ECE 272B Stochastic Processes in Dynamic Systems II; ECE 275A-B Parameter Estimation I & II; ECE 276 B-C Planning & Learning in Robotics, Robot Reinforcement Learning; ECE 285 Special Topics in Robotics & Control Systems; CSE 250A Principles of Artificial Intelligence: Probabilistic Reasoning & Learning; CSE 252A Computer Vision I; MAE 247 Cooperative Control of Multi-Agent Systems; MAE 280A Linear System Theory; MAE 281A Nonlinear Systems.

• Any 4 unit, 200+ course from ECE, CSE, MAE, BENG, CENG, NANO, SE, MATS, MATH, PHYS or CogSci taken for a letter grade may be counted. Exceptions to this list require departmental approval.