“Smart” buildings and highways. Environmental monitoring. Sensor networks. Hybrid systems. The MSE Program in Systems Engineering (SE), grounded in the intersection of electrical and systems engineering, is best positioned to give students the in-depth theoretical foundation and interdisciplinary skills required by the growing complexity of technological systems. Our flexible curriculum allows you to tailor your studies to your personal interests and goals, from signal processing, optimization, simulation, control and cybernetics to complex adaptive systems, stochastic processes and decision sciences.
ratio of SE Master’s students to faculty
Our faculty members are dedicated to building up the next generation of engineers. In addition to being incredible mentors, they’re leading experts and researchers in their fields.
Associate Professor Electrical and Systems Engineering Computer and Information Science
Graph Neural Networks (GNNs) are information processing architectures for signals supported on graphs. They have been developed and are presented in this course as generalizations of the convolutional neural networks (CNNs) that are used to process signals in time and space. The focus of this course is in large scale problems involving high dimensional signals. In these settings fully connected neural networks fail to scale. CNNs are the tool for enabling scalable learning for signals in time and space. GNNs are the tool for enabling scalable learning for signals supported on graphs.
This graduate-level course focuses on continuous and discrete n-dimensional linear systems with m inputs and p outputs in a time domain based on linear operators. The course covers general discussions of linear systems such as, linearization of non-linear systems, existence and uniqueness of state-equation solutions, transition matrices and their properties, methods for computing functions of matrices and transition matrices and state-variable changes. It also includes z-transform and Laplace transform methods for time-invariant systems and Floquet decomposition methods for periodic systems. The course then moves to stability analysis, including: uniform stability, uniform exponential stability, asymptotic stability, uniform asymptotic stability, Lyapunov transformations, Lyapunov stability criteria, eigenvalues conditions and input-output stability analysis. Applications involving the topics of controllability, observability, realizability, minimal realization, controller and observer forms, linear feedback, and state feedback stabilization are included, as time permits. Open to graduates and undergraduates who have taken undergraduate courses in linear algebra and differential equations.
This course is an introduction to human systems engineering, examining the various human factors that influence the spectrum of human performance and human systems integration. We will examine both theoretical and practical applications, emphasizing fundamental human cognitive and performance issues. Specific topics include: human performance characteristics related to perception, attention, comprehension, memory, decision making, and the role of automation in human systems integration.
