Computer Engineering Master of Science Degree

RIT’s Computer Engineering Master’s Degree

The MS in computer engineering provides you with a high level of specialized knowledge in computer engineering. You'll strengthen your ability to successfully formulate solutions to current technical problems while gaining significant independent learning experience that can prepare you for further graduate study or continuing professional development at the leading edge of the field. The master's degree in computer engineering 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 with undergraduate degrees outside of computer engineering.

The degree consists of a required course, flexible core courses, graduate electives, graduate seminar, and your choice of a thesis research or a graduate project. 

Flexible Core: You will choose one course from each of the following core clusters, with your faculty advisor’s guidance.

• Computer Architecture and Digital Design
• Computing, Communications and Algorithms

Graduate Electives: With advisor and department approval, you may request to take graduate courses outside of the department. The graduate electives are selected among the available research tracks. You are encouraged to choose most of your graduate electives within a single track, by consulting with your advisor. You must take a minimum of two electives from the department of computer engineering.

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 seek to answer a fundamental science/engineering question that contributes new knowledge in the field. You are expected to formulate the problem under a faculty advisor’s guidance and conduct extensive quantitative or qualitative analyses with sound methodology. A thesis committee will guide your research activities. Your findings are expected to 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. The project generally addresses an immediate and practical problem, a scholarly undertaking that can have tangible outcomes. Examples of typical projects include implementing, testing, and evaluating a software and/or hardware system or conducting a comprehensive literature review with a comparative study. You are expected to give a presentation or demonstration of the final deliverables of your project. For detailed information on a graduate project timeline and requirements, please refer to Computer Engineering Graduate Project.

Master's in Computer Engineering Research Tracks

You are encouraged to choose most of your graduate electives within a single research track by consulting with your advisor. You may take relevant courses from other academic programs, including electrical engineering, computer science, and software engineering, to support a 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 on 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 has formed our way of life. Ubiquitous access to data using/from these devices with reliable performance as well as presents exciting challenges for engineers and scientists. Being 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 systems that interact 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.