Sorry, you need to enable JavaScript to visit this website.

Site-wide links

Curriculum - Graduate Degree MS

Graduate program chair: Dhireesha Kudithipudi

Program overview

The master of science degree in computer engineering provides students with a highly specialized knowledge in computer engineering, strengthening their ability to successfully formulate solutions to current technical problems, and offering 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.


The degree requires 30 semester credit hours and includes Analytical Topics (CMPE-610), two flexible core courses, four graduate electives, two semesters of graduate seminar, and options to conduct Thesis Research or Graduate Project. The core courses and graduate electives provide breadth and depth of knowledge to the students. The Computer Engineering Graduate Seminar (CMPE-795) provides students with exposure to a variety of topics presented by researchers from within RIT, industry, and other universities, and guides students to choose the culminating experience in either Thesis Research or Graduate Project.

Thesis Research: Independent investigation of a research problem that contributes to the state of the art.

Students who pursue the Thesis option will 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.

Semester MS Thesis Checklist

Graduate Project: Scholarly undertaking that addresses a current technical problem with tangible outcomes

Students who pursue the Project option will 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. The students are expected to give a presentation or demonstration of the final deliverables of the project.

CMPE-610 Analytical Topics is required.  Two courses are chosen from the following flexible graduate core course list with faculty advisor’s guidance.

Semester MS Project Checklist

Flexible Graduate Core:

  • CMPE-630 Digital Integrated Circuit Design
  • CMPE-655 Multiple Processor Systems
  • CMPE-660 Reconfigurable Computing
  • CMPE-670 Data and Communication Networks
  • CMPE-685 Computer Vision

The Graduate Electives shall be selected within the following 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.

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.

Graduate (600-level and above) courses:

  • CMPE-655 Multiple Processor Systems
  • CMPE-660 Reconfigurable Computing
  • CMPE-665 Performance Engineering of Real-Time and Embedded Systems
  • CMPE-731 Design and Test of Multi-core Chips
  • CMPE-750 Advanced Computer Architecture
  • CMPE-755 High Performance Architectures
  • CSCI-654 Foundations of Parallel Computing
  • CSCI-652 Distributed Systems
  • CSCI-742 Compiler Construction




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.

Graduate (600-level and above) courses:

  • CMPE-630 Digital Integrated Circuit Design
  • CMPE-655 Multiple Processor Systems
  • CMPE-730 Advanced Digital Integrated Circuit Design
  • CMPE-731 Design and Test of Multi-core Chips
  • CMPE-750 Advanced Computer Architecture
  • EEEE-602 Random Signals ad Noise
  • EEEE-610 Analog Electronics
  • EEEE-620 Design of Digital Systems
  • EEEE-712 Advanced Field Effect Devices
  • EEEE-713 Solid State Physics
  • EEEE-720 Advanced Topics in Digital Systems Design
  • EEEE-726 Mixed Signal IC Design
  • EEEE-730 Advanced Analog IC Design



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. The research track on Networks and Security in Computer Engineering focuses on intelligent wireless and sensor networks, cryptographic engineering, and predictive cyber situation awareness.

Graduate (600-level and above) courses:

  • CMPE-661 Hardware and Software Design for Cryptographic Applications
  • CMPE-670 Data and Communication Networks
  • CMPE-770 Wireless Networks
  • CMPE-789 Machine Intelligence
  • CSCI-642 Secure Coding
  • CSCI-662 Foundations of Cryptography
  • CSCI-720 Big Data Analytics
  • CSCI-734 Foundations of Security Measurement and Evaluation
  • CSCI-735 Foundations of Intelligent Security Systems
  • CSCI-736 Neural Networks and Machine Learning
  • CSCI-762 Advanced Cryptography
  • CSEC-743 Computer Viruses and Malicious Software
  • CSEC-744 Network Security
  • NSSA-612 Network Modeling and Analysis
  • NSSA-711 Advanced Routing Protocols
  • NSSA-715 Network Design and Performance
  • EEEE-602 Random Signals and Noise
  • EEEE-693 Digital Data Communication
  • EEEE-797 Wireless Communication



Computer Vision and Machine Intelligence:

Visual information is ubiquitous and ever more important for applications such as robotics, healthcare, 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.

Graduate (600-level and above) courses:

  • CMPE-680 Digital Image Processing Algorithms
  • CMPE-685 Computer Vision
  • CMPE-789 Machine Intelligence
  • EEEE-647 Artificial Intelligence Explorations
  • EEEE-670 Pattern Recognition
  • EEEE-685 Principles of Robotics
  • EEEE-780 Digital Video Processing
  • EEEE-781 Image and Video Compression
  • IMGS-756 Advanced Digital Image Processing
  • CSCI-713 Applied Perception in Graphics and Visualization
  • CSCI-715 Applications in Virtual Reality
  • CSCI-719 Topics in Computer Graphics
  • CSCI-720 Big Data Analytics
  • CSCI-731 Advanced Computer Vision



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.

Graduate (600-level and above) courses:

  • CMPE-663 Real-time and Embedded Systems
  • CMPE-664 Modeling of Real-time Systems
  • CMPE-665 Performance Engineering of Real-Time and Embedded Systems
  • EEEE-602 Random Signals and Noise
  • EEEE-610 Analog Electronics
  • EEEE-661 Modern Control Theory
  • EEEE-733 Robust Control
  • EEEE-765 Optimal Control
  • EEEE-768 Adaptive Signal Processing
  • EEEE-793 Error Detection and Error Correction
  • EEEE-794 Information Theory
  • MATH-781 Wavelets and Applications



Additional Graduate Level Math Courses as Electives:

  • ISEE-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



  Rochester Institute of Technology
One Lomb Memorial Drive,
Rochester, NY 14623-5603
Copyright © Rochester Institute of Technology, All Rights Reserved. | Disclaimer | Copyright Infringement