UMBC’s Computer Science and Electrical Engineering Department offers both M.S. and Ph.D. programs in Electrical Engineering. Below you will find information on the Electrical Engineering graduate program, research areas in Electrical and Computer Engineering, as well as information about the application process.
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 Laboratory (MLSP Lab): http://mlsp.umbc.edu
- 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: https://www.csee.umbc.edu/~sjkim
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 & Communications — Faculty 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): http://esnet.cs.umbc.edu/
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.
- 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.
- Tinoosh Mohsenin, Ph.D., Associate Professor, Specialization Areas: Algorithms, architectures and hardware design for high performance and energy-efficient computation that support signal processing, machine learning, knowledge extraction and data driven computing, with expertise in ASIC chip design, FPGAs, GPUs and programmable domain-specific many core hardware design. EEPHPC: http://eehpc.csee.umbc.edu/
- 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: https://eclipse.umbc.edu/robucci/
All students (both PhD and Masters) should take at least five courses from the courses listed under Groups A and B, and at least two of these courses should be from Group A.
Group A Courses (offered every year):
- ENEE 620 Probability and Random Processes
- ENEE 621 Detection and Estimation Theory
- ENEE 630 (Solid-state Electronics) or ENEE 631 (Semiconductor Devices)
- ENEE 680 Electromagnetic Theory
Group B Courses (selected subset is offered every year)
- ENEE 601 Signal and Linear Systems Theory
- ENEE 610 Digital Signal Processing (Cross-listed with CMPE 422)
- ENEE 612 Digital Image Processing
- ENEE 622 Information Theory
- ENEE 639 Neural Engineering and Instrumentation
- ENEE 683 Lasers
- ENEE 684 Introduction to Photonics
- ENEE 712 Pattern Recognition
- ENEE 718 Advanced Topics in Signal Processing
- ENEE/CMPE 605 Applied Linear Algebra
- CMPE 611 Computer Architecture (CMSC 611)
- CMPE 615 Digital Signal Processing Hardware Implementation*
- CMPE 640 Custom VLSI Design
- CMPE 641 Advanced VLSI Design II
- CMPE 645 Computer Arithmetic Algorithms & Implementations
- CMPE 646 VLSI Design Verification and Test
- CMPE 647 Analog IC Design
- CMPE 650 Digital Systems
- CMPE 684 Wireless Sensor Networks
Students must consult with their assigned advisors prior to registration and finalize their course selection with their advisors.
A star next to the course name denotes that new course application is in progress for the given course.
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.
M.S. in Electrical Engineering
Within five years of admission, the student must earn a minimum of 30 credit hours for the thesis option (6 of which are MS thesis research credits ENEE 799) or 33 credit hours (3 of which is the graduate project research ENEE 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 MATH, STAT, CMSC, or from any other related discipline. 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 for this request and must be signed by the student’s research advisor
Ph.D in Electrical Engineering
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 excluding ENEE 66X and ENEE 67X. The remaining four courses (12 credits) can be MATH, STAT, CMSC, or from any other related discipline. 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 for this request and must be signed by the student’s research advisor.
For students who are already in the program (admitted prior to Fall 2012):
- ENEE 601, ENEE 623, and CMPE 691 (Embedded Systems and FPGAs) can each count as a Group B elective.
- The core course requirement, i.e., the courses a student must take from Groups A and B is four rather than five (at least two of these courses must be from Group A). This is true for the PhD portfolio course requirements as well, i.e., students must satisfy the GPA requirement for four core courses rather than five.
Meet the Professors
Dr. Fow-Sen Choa uses a Chemical Vapor Desposition System to grow semiconductors that are used for chemical detection and breath analysis using photo-acoustic (PA) effects. In addition, he has been working with undergraduate students at UMBC on projects dealing with flying robots, Fourier analysis of music instrument, x-ray scan of superlattice crystal growths, and brainwave measurement and analysis. For more information, read his research profile.
Dr. Tulay Adali specializes in statistical signal processing.Since 1992, Dr. Adali has been the director of the Machine Learning for Signal Processing Lab (MLSP-Lab) at UMBC. Currently, she has been working on diagnosing schizophrenia by analyzing functional MRI and other medical imaging data. For more information, read her research profile.
How to Apply
Pre-requisites for Admission
Applicants must have a B.S. degree in Electrical Engineering from an ABET-accredited undergraduate program with a GPA equivalent to ‘B+’ or higher. Individuals whose records indicate strong potential for successful pursuit of the master’s or doctoral degree objectives and who have similar undergraduate preparation with strong academic records in computer science, mathematics, physics or other areas of engineering or science are encouraged to apply (B.S. degrees in engineering technology are not considered equivalent to the B.S. degree in engineering or the B.A. degree in the sciences). Students whose degrees are not in electrical engineering generally will be required to take courses to make up deficiencies in their backgrounds. Students who plan to pursue the Ph.D. degree but who do not already have an M.S. degree are advised to apply for admission to the M.S. program.
The Application Process
Apply online through UMBC’s Graduate School Website. Applicants must also submit:
- An Official Transcript
- 3 Letters of Recommendation
- Statement of purpose (See Preparation Guidelines)
- Graduate Record Examination (GRE) scores or GRE Waiver Form
- TOEFL scores (International students only)
- Fall: January 7th
- Spring: June 1st
- Fall: January 7th (for financial consideration), June 1st
- Spring: June 1st (for financial consideration), November 1st