Computer Engineering Master of science degree

The MS degree in computer engineering provides students with a high level of specialized knowledge in computer engineering, strengthening their ability to successfully formulate solutions to current technical problems, and offers a significant independent learning experience in preparation for further graduate study or for continuing professional development at the leading edge of the discipline. The program accommodates applicants with undergraduate degrees in computer engineering or related programs such as electrical engineering or computer science. (Some additional bridge courses may be required for applicants from undergraduate degrees outside of computer engineering).

Program goals and learning objectives

The MS in computer engineering prepares graduate students to:

• demonstrate independent learning, which is necessary in order to update their skills in a changing workplace and economy, and
• successfully formulate solutions to current technical problems in computer engineering or related disciplines.

The program's student learning outcomes are:

• Tools and Techniques: The ability to utilize state-of-the-art tools and techniques in the field of computer engineering.
• Depth: A depth of knowledge in a specialty area of computer engineering.
• Research: The ability to perform independent research.

Plan of study

The degree requires one required course, two flexible core courses, four graduate electives, two semesters of graduate seminar, and options to conduct thesis research or a graduate project. The core courses and graduate electives provide breadth and depth of knowledge. The Computer Engineering Graduate Seminar (CMPE-795) provides students with exposure to the state-of-the-art research in computer engineering and related disciplines.

Thesis research

Thesis research is an independent investigation of a research problem that contributes to the state of the art. Students who pursue the thesis option take nine semester credit hours of thesis research to answer a fundamental science/engineering question that contributes to new knowledge in the field. Students are expected to formulate the problem under a faculty advisor’s guidance and conduct extensive quantitative or qualitative analyses with sound methodology. The student’s thesis committee must have at least three and no more than four faculty members, including the primary thesis advisor. Two of the committee members must be computer engineering faculty. The findings through thesis research should be repeatable and generalizable, with sufficient quality to make them publishable in technical conferences and/or journals. For detailed information on thesis research timeline and requirements, please refer to Computer Engineering Thesis Research.

Graduate project

The graduate project is a scholarly undertaking that addresses a current technical problem with tangible outcomes. Students who pursue the graduate project option take six semester credits of project focus graduate electives and three semester credits of Graduate Project, to obtain specialized education through additional courses and conduct a professionally executed project under the supervision of a faculty advisor. The project generally addresses an immediate and practical problem, a scholarly undertaking that can have tangible outcomes. Typical projects may implement, test and evaluate a software and/or hardware system, conduct a comprehensive literature review with comparative study, etc. Students are expected to give a presentation or demonstration of the final deliverables of the project. For detailed information on graduate project timeline and requirements, please refer to Computer Engineering Graduate Project.

Flexible core

One course is chosen from each of the following core clusters with faculty advisor’s guidance.

Computer Architecture and Digital Design

• CMPE-630 Digital Integrated Circuit Design
• CMPE-660 Reconfigurable Computing
• CMPE-755 High Performance Architectures

Computing, Communications and Algorithms 

• CMPE-670 Data and Communication Networks
• CMPE-655 Multiple Processor Systems
• CMPE-677 Machine Intelligence

Graduate electives

Computer engineering graduate electives are 600 level and above. With advisor and department approval, students may request to take graduate courses outside of the department. The graduate electives are selected among the available research tracks. Students are encouraged to choose most of their graduate electives within a single track, by consulting with their advisor. Each student must take a minimum of two electives from the department of computer engineering. For graduate level math courses as electives, students may choose from the following:

• SEE-601 Systems Modeling and Optimization
• ISEE-701 Linear Programming
• ISEE-702 Integer and Nonlinear Programming
• MATH-603 Optimization Theory
• MATH-605 Stochastic Processes
• MATH-611 Numerical Analysis
• MATH-651 Combinatorics and Graph Theory I

Research tracks

Students are encouraged to choose most of their graduate electives within a single research track, by consulting with their advisor. Each student must take a minimum of two electives from the department of computer engineering. Students are allowed to take relevant courses from other academic programs, including electrical engineering, computer science, and software engineering, for specific research focus. The following research tracks are available:

• Computer Architecture–Computer architecture area deals with hardware resource management, instruction set architectures and their close connection with the underlying hardware, and the interconnection and communication of those hardware components. Some of the current computer architecture challenges that are being tackled in the computer engineering department include energy efficient architectures, high performance architectures, graphic processing units (GPUs), reconfigurable hardware, chip multiprocessors, and Networks-on-Chips.
• Computer Vision and Machine Intelligence–Visual information is ubiquitous and ever more important for applications such as robotics, health care, human-computer interaction, biometrics, surveillance, games, entertainment, transportation and commerce. Computer vision focuses on extracting information from image and video data for modeling, interpretation, detection, tracking and recognition. Machine Intelligence methods deal with human-machine interaction, artificial intelligence, agent reasoning, and robotics. Algorithm development for these areas spans image processing, pattern recognition and machine learning, and is intimately related to system design and hardware implementations. 
• Integrated Circuits and Systems–Modern processors demand high computational density, small form factors, and low energy dissipation with extremely high performance demands. This is enabled by the nanoscale and heterogeneous integration of transistors and other emerging devices at the massive-scale. Such nanocomputers will open unimaginable opportunities as well as challenges to computer engineers. This research focuses designing computers with emerging novel technologies in the presence of severe physical constraints; investigating dynamic reconfigurability to exploit the power of nano-scale electronics for building reliable computing systems; and studying the applicability of emerging technologies to address challenges in computing hardware of the future.
• Networks and Security–The prevalence of interconnected computing, sensing and actuating devices have transformed our way of life. Ubiquitous access to data using/from these devices with reliable performance as well as security assurance presents exciting challenges for engineers and scientists. Resilient to environmental uncertainty, system failures and cyber attacks requires advances in hardware, software and networking techniques. This research track focuses on intelligent wireless and sensor networks, cryptographic engineering, and predictive cyber situation awareness. 
• Signal Processing, Control, and Embedded Systems–This research area is concerned with algorithms and devices used at the core of system that interacts with our physical world. As such, this area considers the sensing, analysis and modeling of dynamic systems with the intent of measuring information about a system, communicating this information and processing it to adapt its behavior. Application areas are robust feedback-based control where uncertainty in the dynamics and environment must be considered during the design process and signal processing algorithms and devices for system sensing and adaptation.