Computer Engineering (M.S. & Ph.D.)

There is a limited number of application fee waivers for Ph.D. applicants to the Computer Engineering or the Electrical Engineering programs for Fall 2024. ONLY applications submitted before or on December 22, 2023, would qualify. To waive the fee, please contact Ms. Keara Fliggins at to get a special code. Note that in case the code is used for the Master of Science programs or after December 22, 2023, the application will not be considered unless the fee is paid.

UMBC’s Computer Science and Electrical Engineering Department offers both M.S. and Ph.D. programs in Computer Engineering. Below you will find information on the Computer Engineering graduate program, research areas in Electrical and Computer Engineering, as well as information about the application process.

Research Areas

Signal Processing & Machine Learning — Faculty in this area conduct research developing new platforms and methods to address many of the challenges posed by today’s data-rich applications, especially addressing problems in the complex and big data realm. The application domains are many and include problems in medical image analysis and data fusion, remote sensing, image processing for hyperspectral data, cognitive radio networks and future power systems (smart grids).

  • Tülay Adali, PhD., Distinguished University Professor, Specialization Areas: Statistical and adaptive signal processing, machine learning, matrix and tensor factorizations, and their applications in multimodal and multi-set data fusion and medical image analysis. Machine Learning for Signal Processing Lab:
  • Chein-I Chang, Ph.D., Professor, Specialization Areas: Hyperspectral imaging, remote sensing signal and image processing, medical imaging.
  • Seung-Jun Kim, Ph.D., Assistant Professor, Specialization Areas: Statistical signal processing, optimization, machine learning, and big data techniques with applications to wireless communications/networking, future power systems/smart grids, brain/medical data analysis. Signal Processing and Smart Systems Laboratory:

Microelectronics/ Microsystems (MEMS) & Photonics — Faculty in this area conduct research in the complementary fields of electronic, bioelectronic, nanotechnology, electromagnetic, and optical devices and circuits, with broad application to the next generation light emitters, power electronics, wearable and implantable biomedical sensors that advance consumer, industrial, national security, and health care outcomes.

  • Fow-Sen Choa, PhD., Professor, Specialization Areas: Material growth, nanofabrication, Near and Mid-IR lasers and detectors, RF-photonic components and systems, photoacoustic sensing, EEG brain function analysis and monitoring, transcranial magnetic, direct/alternating current stimulations, dynamic brain network analysis.
  • Li Yan, Ph.D., Professor, Specialization Areas: Ultrafast optics, solid-state lasers, optical communications, nonlinear optics, quantum optics, coherent beam combining and mixing.

Optics & CommunicationsFaculty in this area conduct basic and applied research that relies on the synergy of physics, materials science, numerical modeling, and device applications to understand and develop innovative materials, devices, and algorithms that addresses the demand for higher data transfer rates and bandwidths, and next generation mobile/wireless technologies.

  • Gary Carter, Ph.D., Professor, Specialization Areas: Optoelectronics; diode lasers; nonlinear optics; optical communications.
  • Anthony Johnson, PhD., Professor, Specialization Areas: Ultrafast photophysics and nonlinear optical properties of bulk, nanostructured, and quantum well semiconductor structures, ultrashort pulse propagation in fibers and high-speed lightwave systems.
  • Curtis Menyuk, Ph.D., Professor, Specialization Areas: Lasers, computational modeling of photonic systems, time and frequency generation and transfer, lightwave communications, optical fibers, optical networks, and nonlinear phenomena.
  • Mohamed Younis, Ph.D., Associate Professor, Specialization Areas: Wireless networks; Cyber-physical systems, internet of things, fault tolerant computing, embedded computer systems, and secure communication. Embedded Systems and Networks Lab (ESNET):

VLSI Systems/ Hardware Security & Digital design Faculty in this area are working on advanced computer-aided VLSI chip design, and developing innovations in VLSI hardware testing, security, computational and communication protocols, and sensor-processing integration that protect the security and integrity of hardware systems that meet the challenges for ultrafast and low-power computing, real-time and secure cyber-physical systems, and effective methods for processing complex data and enhancing multicore and cloud computing.

  • Riadul Islam, Ph.D., Assistant Professor, Specialization Areas:  VLSI CAD tools and low-power digital and mixed-signal IC design, alternative current-mode clock network design to vehicular network security, soft-/hard-error robust, secured IC design to neuromorphic computing and machine learning applications in computing.
  • Naghmeh Karimi, Ph.D., Assistant Professor, Specialization Areas: Hardware Security & Design-for-Trust, Fault Tolerance & Design-for-Reliability, Hardware Testing & Design-for-Testability, Hardware Design & Synthesis, and VLSI Design.
  • Dhananjay Phatak, Ph.D., Associate Professor, Specialization Areas: Computer arithmetic algorithms and implementations, all aspects of computer/cyber security, number theory, computer networks and neural nets.
  • Ryan Robucci, PhD., Associate Professor, Specialization Areas: Analog and mixed-signal VLSI; sensors and cyber-physical systems, human-computer systems, and hardware security. ECLIPSE:

Program Requirements

All students (both PhD and Masters) must take at least five courses from the courses listed under Groups A and B, and at least two of these courses must be from Group A.

Group A Courses (offered every year):

  • CMPE 611/CMSC 611            Computer Architecture
  • CMPE 640                                Custom VLSI Design
  • CMPE 650                                Digital Systems

Group B Courses (selected subset is offered every year)

  • CMPE 615 Digital Signal Processing Hardware Implementation*
  • CMPE 641 Advanced VLSI Design II
  • CMPE 645 Computer Arithmetic Algorithms & Implementation
  • CMPE 646 VLSI Design Verification and Test
  • CMPE 647 Analog IC Design
  • CMPE 684 Wireless Sensor Networks
  • ENEE/CMPE 605 Applied Linear Algebra
  • ENEE 601 Signal and Linear Systems Theory
  • ENEE 610 Digital Signal Processing (Cross-listed with CMPE 422)
  • ENEE 612 Digital Image Processing
  • ENEE 620  Probability and Random Processes
  • ENEE 621  Detection and Estimation Theory
  • ENEE 622  Information Theory
  • ENEE 630  Solid-state Electronics
  • ENEE 631 Semiconductor Devices
  • ENEE 639 Neural Engineering and Instrumentation
  • ENEE 680  Electromagnetic Theory
  • ENEE 683 Lasers
  • ENEE 684 Introduction to Photonics
  • ENEE 712 Pattern Recognition

*New course application is in progress.

Students must consult with their assigned advisors prior to registration and finalize their course selection with their advisors. All courses need to be approved by the student’s advisor.

Some instances of CMPE and ENEE 691 may be designated as group B electives; please check with your advisor prior to taking such a course.

Masters: Within five years of admission, the student must earn a minimum of 30 credit hours for the thesis option (six of which are MS thesis research credits CMPE 799) or 33 credit hours (three of which are the graduate project research CMPE 698) for the non-thesis (w/project) option. All M.S. students must choose either the thesis or non-thesis (w/project) option: there is no course-only option.

At least six of these courses (18 credits) (for both Masters options) must be graduate ENEE or CMPE courses, i.e., ENEE/CMPE courses at the 600 or 700 level. The remaining four courses (12 credits for MS w/project) and two courses (six credits for MS thesis) can be CMSC, MATH, STAT, or from any other related discipline at the 600-level or above with the following exception: a maximum of two 400 level courses (six credits) are allowed in MATH/STAT only, and a maximum of three credits of Independent Study (ENEE/CMPE 699) are allowed.

Students must receive a grade of B or better in two of the Group A courses.

Requests for approval of non-CMPE/ENEE course credits must be submitted before registering for the course. There is a form available (HERE) for this request and must be signed by the student’s research advisor.

Ph.D.: Students are required to take a minimum of 11 courses (33 credits) beyond the bachelor’s degree. At least seven of these courses (21 credits) must be graduate ENEE or CMPE courses, i.e., ENEE/CMPE courses at the 600 or 700 level. The remaining four courses (12 credits) can be CMSC, MATH, STAT, or from any other related discipline at the 600-level or above with the following stipulation and exception:

  • Only three credits of Independent Study (ENEE/CMPE 699) can count toward the total course requirement.
  • Students who have received their Masters at UMBC are allowed to count two 400 level MATH/STAT courses for the PhD degree with approval of their advisors.

The doctoral dissertation must be an original and substantive contribution to knowledge in the student’s major field. It must demonstrate the student’s ability to carry out a program of research and to report the results in accordance with standards observed in the recognized scientific journals related to that field.

Doctoral students must: (a) submit their PhD Comprehensive Portfolio and receive a pass grade (P) within four (4) semesters of entrance to the program (six (6) semesters for part-time students); (b) develop and defend a doctoral dissertation proposal and be admitted to doctoral candidacy within four (4) years of entrance to the program (five (5) years for part-time students); and (c) complete all Ph.D. requirements for their field of specialty within four (4) years of admission to doctoral candidacy.

Students must receive a grade of B or better in two of the Group A courses.

Requests for approval of non-CMPE/ENEE course credits must be submitted before registering for the course. There is a form available (HERE) for this request and must be signed by the student’s research advisor.

For students who are already in the program (admitted prior to Spring 2012):

  • CMPE 691 (Embedded Systems and FPGAs), ENEE 601, and ENEE 623, can each count as a Group B elective.
  • The minimum number of ENEE/CMPE course requirement is four.


Click here for a complete list of Computer Engineering Faculty.

Click here for the PhD Comprehensive Portfolio.

How to Apply

Pre-requisites for Admission

An applicant to a graduate program in Computer Engineering is expected to have a strong back­ground in computer engineering and mathematics courses. This includes Calculus I and II, Linear Algebra, Differential Equations and Probability & Statistics in Mathematics. Applicants are expected to have taken the equivalent of the following UMBC courses:

  • CMPE 212: Principles of Digital Design
  • CMPE 306: Basic Circuit Theory
  • CMPE 310: Systems Design and Programming
  • CMPE 314: Electronic Circuits
  • CMPE 315: Principles of VLSI Design
  • CMSC 341: Data Structures
  • CMSC 411: Computer Architecture

The Application Process

Apply online through UMBC’s Graduate School Website. Applicants must also submit:

  • An Official Transcript
  • Three Letters of Recommendation
  • Statement of purpose (See Preparation Guidelines)
  • Graduate Record Examination (GRE) scores or GRE Waiver Form
    (GRE is mandatory to be considered for departmental support.)
  • TOEFL scores (International students only)

Application Deadlines

International Students

  • Fall: January 7th
  • Spring: June 1st

Domestic Students

  • Fall: January 7th (for financial consideration), June 1st
  • Spring: June 1st (for financial consideration), November 1st

Apply Now!

Further details can be found by following these links: