This post outlines the requirements and structures for three primary graduate programs in the ECE Department during the 2026-2027 academic year:
- Master of Science in Robotics Engineering (MS ERE)
- Master of Science in Electrical and Computer Engineering (MS EECE)
- PhD in Electrical and Computer Engineering (PhD ECE)
Program Comparison Summary
| Feature | MS Robotics (MS ERE) | MS ECE (MS EECE) | PhD ECE (with MS) | PhD ECE (Direct/no MS) |
| Total Credits | 30 | 30 | 30 | 60 |
| EE Course Credits MIN |
Coursework – 15 Report – 12 Thesis – 10 |
Coursework – 15 Report – 12 Thesis – 10 |
9-12 | 21 |
| 3000-level Courses | Not permitted | Not permitted | Not permitted | Not permitted |
| 4000-level MAX |
Coursework – 12 Report – 12 Thesis – 10 |
Coursework – 12 Report – 12 Thesis – 10 |
0-3 | 6-8 |
| Max Co-op Credits | 3 credits (UN 5000-5003) | 3 credits (UN 5000-5003) | 3 credits (UN 5000-5003) | 3 credits (UN 5000-5003) |
| RCR Requirement | 1-3 credits (Report/Thesis) | 1-3 credits (Report/Thesis) | 1-3 credits | 1-3 credits |
| Typical Duration | 1.5 – 2 Years | 1.5 – 2 Years | 3 – 5 Years | 4 – 6 Years |
Academic Flexibility & Course Load
Unlike undergraduate degree programs, graduate degrees are more flexible, allowing students to tailor their plan of study to their specific goals and interests. A Master of Science student may select courses of their choosing, provided they conform to the degree requirements. The standard course load for a graduate student is nine (9) credits per semester. Please contact the ECE Graduate Program Director to schedule an advising session.
Online Course Registration Guidelines
Specific regulations govern online enrollment for on-campus graduate students:
- Dual-Delivery Courses (+OL): Online sections of courses offered concurrently with on-campus sections are unavailable to on-campus students.
- CPT Exception: On-campus students participating in Curricular Practical Training (CPT) are permitted to enroll in these online course sections.
- Exclusively Online Courses: Graduate courses that are offered only in an online format (without an equivalent on-campus section) are open to on-campus students.
Fall 2026 Graduate Course Recommendations & Class Descriptions
To assist with academic planning and elective registration, the active graduate-level courses offered in Fall 2026 are listed below. Courses covering multi-disciplinary domains are cross-listed in all applicable technical areas. The indicator (+OL) identifies courses that offer online enrollment options.
1. Power & Energy
Focus: Power transmission, conversion, machines, and smart-grid integration.
- EE 4221 – Power System Analysis 1 (+OL) Covers power transmission line parameters and applications, symmetrical components, transformer and load representations, system faults and protection, and the per-unit system.
- EE 4227 – Power Electronics (+OL) Fundamentals of circuits for electrical energy processing. Covers switching converter principles for dc-dc, ac-dc, and dc-ac power conversion. Other topics include harmonics, pulse-width modulation, feedback control, magnetic components, and power semiconductors.
- EE 5200 – Advanced Methods in Power Systems (+OL) Advanced analysis and simulation methods for load flow, symmetrical components, short-circuit studies, optimal system operation, stability, and transient analysis. Application of commonly used software reinforces concepts and provides practical insights.
- EE 5230 – Power System Operations (+OL) Study of advanced engineering and economic algorithms and analysis techniques for the planning, operation, and control of the electric power system from generation through transmission to distribution.
- EE 5900 – Modern Power System Dynamics (+OL) Focuses on the dynamic behavior, stability analysis, modeling, and transient control of modern interconnected electrical networks.
- EE 5900 – High Voltage Engineering (+OL) Examines insulation engineering, breakdown mechanisms in solids, liquids, and gases, overvoltages, testing methods, and high-voltage safety procedures.
2. Signals & Systems
Focus: Processing, analysis, systems modeling, and communications engineering.
- EE 4252 – Digital SP and Applications Digital signal processing techniques with emphasis on applications. Includes sampling, the Z-transform, digital filters, and discrete Fourier transforms. Emphasizes techniques for design and analysis of digital filters. Special topics may include the FFT, windowing techniques, quantization effects, physical limitations, image processing basics, image enhancement, image restoration, and image coding.
- EE 5500 – Prob & Stoch Processes (+OL) Theory of probability, random variables, and stochastic processes, with applications in electrical and computer engineering. Probability measure and probability spaces. Random variables, distributions, expectations. Random vectors and sequences. Stochastic processes, including Gaussian and Poisson processes. Stochastic processes in linear systems. Markov chains and related topics.
- EE 5715 – Linear Systems Theory & Design (+OL) Overview of linear algebra, Modern Control: state-space based design of linear systems, observability, controllability, pole placement, observer design, stability theory of linear time-varying systems, Lyapunov stability, optimal control, Linear Quadratic regulator, Kalman filter, Introduction to robust control.
- ME 4775 – Control Sys Analysis & Design (+OL) This course covers topics of control systems design. Course includes a review for modeling of dynamical systems, stability, and root locus design. Also covers control systems design in the frequency domain, fundamentals of digital control and nonlinear systems.
- ME 5670 – Experimental Design in Engg (+OL) Review of basic statistical concepts. Models for testing significance of one or many factors. Reducing experimental effort by incomplete blocks and Latin squares. Factorial and fractional factorial designs. Response surface analysis for optimal response.
3. ElectroPhysics
Focus: Physical systems, devices, electromagnetic fields, and semiconductor material processing.
- EE 4490 – Laser Systems and Applications Survey of laser types and analysis of common physical and engineering principles, including energy states, inversion, gain, and broadening mechanisms, from a quantum-mechanical perspective. Laser applications and laser properties are explored in the laboratory portion.
- EE 5330 – Chip Fabrication This course provides an advanced introduction to the science and engineering involved in semiconductor device fabrication associated with microelectronic chips through lecture and laboratory exercises.
4. Computer Engineering
Focus: Processing hardware, algorithms, networking, and automotive computer systems.
- EE 4173 – Comp Sys Engineering & Perform Covers the principles and practices of modern computer architecture. Emphasizes quantitative performance evaluation of: memory hierarchies, from cache through virtual memory; pipelined processors with advanced hazard management; and combined processor/memory systems. Introduces RAID, superscalars, parallel processing, cache coherence, and performance simulation software.
- EE 4271 – VLSI Design Design of VLSI circuits using CAD tools. Analysis of physical factors affecting performance. Exhibit content learning through a course project demonstration.
- EE 4272 – Computer Networks Computer network architectures and protocols; design and implementation of datalink, network, and transport layer functions. Introduction to the Internet protocol suite (TCP, UDP, IP), domain name service and protocols, file sharing protocols, wireless networks, and network security.
- EE 5271 – VLSI Design Design of VLSI circuits using CAD tools. Analysis of physical factors affecting performance. Exhibit content learning through a course project demonstration.
- EE 5455 – Cybersecurity Indust Ctrl Sys (+OL Only) General introduction to cybersecurity of industrial control systems and critical infrastructures. Topics include NIST and DHS publications, threat analysis, vulnerability analysis, red teaming, intrusion detection systems, industrial networks, industrial malware, and selected case studies.
5. Robotics
Focus: Industrial automation, autonomous perception, intelligence algorithms, control design, and embedded architectures.
- EE 4235 – Sensing/Processing in Robotics Sensing and signal processing for robotics applications in manufacturing and autonomous navigation. Heavy emphasis on developing, testing, and evaluating algorithms. MATLAB programming required.
- EE 5715 – Linear Systems Theory & Design (+OL) Overview of linear algebra, Modern Control: state-space based design of linear systems, observability, controllability, pole placement, observer design, stability theory of linear time-varying systems, Lyapunov stability, optimal control, Linear Quadratic regulator, Kalman filter, Introduction to robust control.
- EE 5821 – Computational Intelligence This course covers the four main paradigms of Computational Intelligence, viz., fuzzy systems, artificial neural networks, evolutionary computing, and swarm intelligence, and their integration to develop hybrid systems. Applications of Computational Intelligence include classification, regression, clustering, controls, robotics, etc.
- EE 5900 – Machine Learning for Robotics Introduces foundational machine learning paradigms and algorithms, deep neural network designs, and training procedures tailored for robot control, vision, and autonomous decision-making processes.
- ME 4707 – Autonomous Systems The main concepts of autonomous systems will be introduced including motion control, navigation, and intelligent path planning and perception. This is a hands-on project based course. Students will have the opportunity to work with mobile robotics platforms. Having a foundational understanding of programming is recommended to make the most of this course.
6. Automotive
Focus: Powertrains, hybrid architecture, electric vehicles, vehicle modeling, and dynamic system optimization.
- EE 4295 – Intro Propulsion Sys for HEV (+OL) Hybrid electric drive vehicle analysis will be developed and applied to examine the operation, integration, and design of powertrain components. Model based simulation and design is applied to determine vehicle performance measures in comparison to vehicle technical specifications. Power flows, losses, energy usage, and drive quality are examined over drive-cycles via application of these tools.
- EE 5811 – Automotive Systems (+OL) Automotive systems for light-duty vehicles are examined from the perspectives of requirements, design, technical, and economic analyses to meet advanced mobility needs. This course links the content for the automotive systems graduate certificate in controls, powertrain, vehicle dynamics, and connected and autonomous vehicles.
- ME 4775 – Control Sys Analysis & Design (+OL) This course covers topics of control systems design. Course includes a review for modeling of dynamical systems, stability, and root locus design. Also covers control systems design in the frequency domain, fundamentals of digital control and nonlinear systems.
- ME 5680 – Optimization I Provides introductory concepts to optimization methods and theory. Covers the fundamentals of optimization, which is central to any problem involving engineering decision making. Provides the tools to select the best alternative for specific objectives.
