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ANUPAM JOSHI, Chair
MOHAMED YOUNIS, Graduate Program Director

Degrees Offered

M.S.(THESIS AND NON-THESIS PROJECT), Ph.D. (Degree Types )

Faculty

Professors-Emeritus

PINKSTON, JOHN T. Ph.D., Massachusetts Institute of Technology; Information Assurance, Superconducting Electronics, Digital Signal Processing.

Associate Professors

PATEL, CHINTAN, Ph.D., University of Maryland, Baltimore County; VLSI design and test
PHATAK, DHANANJAY, Ph.D., University of Massachusetts, Amherst; Mobile and high-performance computer networks; computer arithmetic algorithms and their VLSI implementations; signal processing; neural networks, their applications and efficient implementations; digital and analog VLSI design; and CAD.
ROBUCCI, RYAN, Ph.D., Georgia Institute of Technology; Analog and mixed-signal VLSI; CMOS image sensors; programmable and reconfigurable circuits; sensor interfacing and networking; image processing; real-time, mixed-mode signal processing; biologically-inspired systems; information theory; and computer-aided design and analysis of complex mixed-signal processing systems.
SLAUGHTER, GYMAMA, Ph.D., Virginia Commonwealth University; Sensor-processor integration, bioelectronics design and theory, optimization methods for physical circuit design, biologically inspired computing (neural networks), and sensor interfacing and wireless networking and communication. Other research areas include: bioengineering, biosensors, BioMEMS, and fluidic devices.
YOUNIS, MOHAMED, Ph.D., New Jersey Institute of Technology; Sensor Networks, wireless communication; fault-tolerant computing, information security, cyber-physical systems.

Assistant Professors

KARIMI, NAGHMEH, Ph.D., University of Tehran, Tehran, Iran; Hardware security & design-for-trust, fault tolerance & design-for-reliability, hardware testing & design-for-testability, hardware design & synthesis, VLSI design, computer architecture, EDA Tools.
KIM, SEUNG-JUN, Ph.D., University of California, Santa Barbara; Statistical signal processing, optimization, and statistical learning, with applications to wireless communication and networking, future power systems, and big data analytics.
MOHSENIN, TINOOSH, Ph.D., University of California, Davis; High performance and energy-efficient VLSI computation that support communication, signal processing, error correction, and biomedical applications; These include algorithm and architecture enhancements, application mapping/software development on many-core architectures, VLSI design of ASICs and reconfigurable architectures, single-chip solutions targeted for low-power embedded systems through a co-design of programmable cores and application-specific processors.
ZHU, TING, Ph.D., University of Minnesota:  Big Data, Embedded Systems, Cyber-Physical Systems, Mobile Systems, Distributed Systems, Operating Systems, Power Systems, Renewable and Sustainable Energies, Internet of Things, Wireless and Sensor Networks, Network Protocols, Social Networks, and Security

Program Description

The department offers a graduate program leading to the M.S. and Ph.D. degrees in Computer Engineering. The program provides advanced instruction and research opportunities in a broad range of computer engineering areas and is focused on both the theoretical and practical aspects of the state of the art in computer engineering. The doctoral program emphasizes research as a major element of its degree requirements. Fields of specialization in computer engineering supported within the department include:

1. 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).
2. 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.
3. 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.
4. 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.

More details can be found on http://www.csee.umbc.edu/graduate/computer-engineering-ms-phd-2/.

Program Admission Requirements

When seeking admission to the graduate program, applicants must satisfy all entrance requirements of the Graduate School at UMBC. All original application materials must be sent directly to the Graduate School, not to graduate program. Applications are not processed until all documents and fees are received. All applicants must submit official transcripts, three letters of recommendation, statement of purpose, Graduate Record Examination (GRE General Test) scores and, for inter-national students, scores for the TOEFL. Application deadlines are specified by the Graduate School.

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

Students may apply for admission to either the M.S. or the Ph.D. program. However, admission to the Ph.D. program is highly selective, and only students with exceptional backgrounds will be accepted. Students who plan to pursue the Ph.D. degree but who do not already have a master’s in computer science are advised to apply for admission to the M.S. program. New students will be assigned an academic advisor who can provide advice on choosing courses, degree requirements and other important matters during the first year. By the end of the first year, students are expected to have located a faculty member to serve as the research advisor for master’s or doctoral. research. Consideration for continued financial assistance depends on locating a research advisor. Admission to the M.S. and Ph.D. degree programs are separate.

Facilities and Special Resources

The CMPE program facilities include dedicated computer engineering laboratories that provide computers and test and measurement equipment. The department also provides dedicated servers that allow students to use commercial design software, and the Office of Information Technology (OIT) has more than 400 workstations for general student use and several high-end computing systems.

Financial Assistance

Financial aid is available on a competitive basis to a limited number of qualified graduate students in the form of graduate teaching assistantships (TAs), graduate research assistantships (RAs), work-study positions and hourly employment as graders. Graduate RAs are often available to students actively engaged in their master’s thesis or doctoral dissertation research and are awarded and renewed subject to availability of funds and satisfactory research progress. Students are encouraged to apply directly to nationally awarded fellowship programs.

Course Listing

For CMPE (also CMSC and ENEE) course descriptions, and current year special topic course listings and descriptions, see the CSEE Graduate Program(s) website www.cs.umbc.edu/programs/graduate/ or CSEE Department website www.csee.umbc.edu. The set of CMPE 691  courses address specialized computer engineering topics representing the research focus of the faculty, and are scheduled according to student and faculty interests.

Programs

• Computer Engineering, M.S.

• Computer Engineering, Ph.D.

Courses

• CMPE 605 - Applied Linear Algebra
• CMPE 611 - Advanced Computer Architecture
• CMPE 640 - Custom VLSI Design
• CMPE 641 - Topics in VLSI
• CMPE 645 - Computer Arithmetic Algorithms and Implementations
• CMPE 646 - VLSI Design Verification and Test
• CMPE 647 - Analog Integrated Circuit Design
• CMPE 650 - Digital Systems Design
• CMPE 670 - Biomedical Microsystems
• CMPE 684 - Wireless Sensor Networks
• CMPE 685 - Principles of Communications Networks
• CMPE 691 - Special Topics in Computer Engineering
• CMPE 698 - Research Project in Computer Engineering
• CMPE 699 - Independent Study in Computer Engineering
• CMPE 791 - Advanced Topics in Computer Engineering
• CMPE 799 - Master’s Thesis Research
• CMPE 800 - Graduate Research
• CMPE 898 - Pre-Candidacy Doctoral Research
• CMPE 899 - Doctoral Dissertation Research

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