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Thank
you for your recent inquiry about the graduate program in Computer
Science, Electrical Engineering, or Computer Engineering. UMBC—a major research
university in the Baltimore‑Washington area—offers graduate students an
exciting environment for advanced study. With 10,000 undergraduate and graduate
students in the liberal arts, sciences, engineering, and public policy, UMBC is
large enough to provide students with excellent training and research
experience and small enough for close student‑faculty interaction. The
University is a growing center for research and development and technology
commercialization. Campus research grants and training contracts have topped
$44 million, up from just $10 million five years ago. Patent applications by
UMBC researchers have more than quadrupled in three years and more than forty
University‑developed technologies are available for licensing. Adjacent
to the campus, The UMBC Technology Center houses private research, development,
and training organizations seeking interaction with University faculty,
students, and research facilities.
In the Computer Science/Electrical Engineering
Department, graduate programs lead to either a M.S. or Ph.D. degree. These
programs are strongly supported by the industrial companies in the Greater
Baltimore-Washington area, and various collaborative arrangements with
governmental and private institutions make state-of-the-art research facilities
in the area available to UMBC faculty and students.
At UMBC=s
We look forward to hearing from you and wish you
success in your graduate studies.
The Graduate Admissions Committee
www.umbc.edu/engineering/csee/
The
Department of Computer Science and Electrical Engineering (CSEE) offers
separate degree programs in Computer Science (CS), in Electrical Engineering
(EE), and in Computer Engineering (CE). Each of these degree programs offers an
M.S. (with or without a thesis) or Ph.D. degree path. Admission to each program
is separate.
The
Department offers a graduate program leading to the M.S. and Ph.D. degrees in
Computer Science. This program provides advanced instruction, training, and
research opportunities that prepare students for careers and that foster
marketable skills in business, industry, academia, and government agencies. The
program reflects state of the art knowledge in major theoretical and applied
aspects of computation and its applications.
Fields
of specialization in CS include:
Algorithms,
theory and scientific computation (analysis of algorithms, algebraic coding theory,
combinatorial optimization, computational complexity, cryptology, parallel
computing).
Computer networks and
systems
(computer and communication security, distributed systems, networks, massive
storage systems, optoelectronic IC, parallel and distributed process, VLSI
device testing).
Databases, information, and
knowledge management
(artificial intelligence, database systems, digital library, electronic
commerce, information retrieval, intelligent information systems, knowledge
representation and reasoning, machine learning, neural networks, reasoning
under uncertainty).
Graphics,
animation, and visualization (animation, interactive 3D graphics, physically‑based
modeling, procedural modeling, volumetric visualization and rendering).
Program
Admission & Financial Assistance
General
Policy: When seeking admission to the graduate program,
applicants must satisfy all entrance requirements of the
Application deadlines are specified by the
|
Spring semester ‑ November 1 International students and permanent
residents who are attending or have attended a foreign school: Fall semester ‑ January 1 Spring semester ‑ June 1 of
the prior calendar year |
The application review process will begin by
February 1 for admission to the Fall semester and by
October 1 for admission to the following Spring semester. Favorable
consideration will be given to applications received early in each review
cycle. It is the policy of the Department to admit students based solely on
their academic and research performance.
An applicant to the graduate program in
Computer Science is expected to have a strong background in computer science
and mathematics courses. This includes Calculus I and II, Linear Algebra, and
at least one more advanced course in mathematics. In addition, applicants are
expected to have taken the equivalent of the following computer science courses
at UMBC:
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CMSC 203 Discrete Structure
CMSC 313 Computer Organization &
Assembly Language CMSC 331 Principles of Programming
Languages CMSC 341 Data Structures CMSC 411 Computer
Architecture CMSC 421 Principles of Operating Systems CMSC 441 Algorithm Design and Analysis CMSC 451 Automata Theory and Formal
Languages |
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 the student with an exceptional background will be
accepted. Students who plan to pursue the Ph.D. degree but who do not already
have an M.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 choice of courses, degree requirements, and other important matters
during the first year. By the end of the first year, students should seek a
faculty member to serve as the research advisor for the M.S. or Ph.D. research.
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) and graduate research assistantships
(RAs). Preference for TAs is given to first year Ph.D. applicants. Graduate RAs
are often available to students actively engaged in M.S. thesis or Ph.D.
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.
Requirements for the Master
of Science (M.S.) Degree: Within
five years of admission, the student must earn a minimum of thirty (30) credit
hours with thesis option or thirty three (33) credit hours with non‑thesis
option. The student must satisfy the GPA and course requirements for his/her
field of specialty. Each student must complete either a thesis or a scholarly
paper.
The thesis option in the student’s field
requires a minimum of eight (8) graduate‑level courses (24 credit hours)
and six (6) credit hours of thesis (CMSC 799). The thesis must be defended with
an oral exam and accepted by the student’s M.S. thesis committee. A bound copy
of the thesis must be submitted to the department.
The non‑thesis option in the student’s
field requires a minimum of ten (10) graduate level courses (30 credit hours)
and three (3) credit hours of CMSC 698 Research Project work resulting in a
scholarly paper that must be approved by the advisor and read by another
faculty member. A copy of the scholarly paper must be submitted to the
department.
Required Courses: Breadth courses provide a uniform background
and a minimum breadth requirement for graduate students. There are four breadth
courses required for each field which constitute the core requirement. The
number of breadth courses is intentionally designed to be small to offer
maximum course planning flexibility to professors and graduate students. Each
student must take and receive a grade of “B” or better in each of the following
three breadth courses: CMSC 611 Architecture, CMSC 621 Operating Systems, and
CMSC 641 Algorithms. In addition each student must take one of the following six
courses: CMSC 635 Graphics, CMSC 651 Automata Theory, CMSC 655 Numerical
Computation, CMSC 661 Database, CMSC 671Artificial Intelligence, or CMSC 681
Computer Network Architecture.
Research Courses: Research courses provide the course credits
for the student’s research activities, e.g., independent study, graduate
project, scholarly paper, and M.S. thesis.
Additional Courses: Beyond the four breadth courses, the thesis
student must take six credits of CMSC 799 (Master’s Thesis) and a minimum of
twelve additional course credits. For students with non-thesis option, the
minimum additional course credit is eighteen and three credits of CMSC 698
Research Project. Each field of specialty may specify courses that can be used
to satisfy this requirement. A student may request to take a maximum of six
credits of coursework outside the Department. These courses must be graduate
level and approved by the student’s advisor and the Graduate Program Director
prior to registration. A total of six credits towards the degree can be taken
pass/fail.
Comprehensive Examination: The Comprehensive Examination is not required
for M.S. students. However, it is necessary for anyone pursuing or planning to
pursue a Ph.D.
M.S. Thesis: Any student may undertake Master’s Thesis,
supervised by a faculty member as the thesis advisor. Upon completion of the
thesis research, the thesis must be defended in an oral presentation.
Transfer Credits: No more than six credits
may be transferred from another university. Credit transfer must be approved by
the Director of the Graduate program.
Requirements for the Doctor
of Philosophy
Requirements for the Doctor
of Philosophy (Ph.D.) Degree: Each field of specialty sets its course requirements
for Ph.D. students in that field. The Department’s minimum requirement is
eleven (11) courses excluding graduate seminar participation, graduate research
credits prior to Ph.D. candidacy, and doctoral dissertation research credits
(CMSC 899). The doctoral dissertation
must be an original and substantive contribution to knowledge in the student’s
major field and 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.
The Ph.D. student must:
1.
Pass
the written comprehensive exam within four semesters of entrance to the program
(five semesters for part‑time students)
2.
Develop
and defend a doctoral dissertation proposal and be admitted to Ph.D. candidacy
within four years of entrance to the program (five years for part‑time
students)
3.
Complete
all Ph.D. requirements for their field of specialty within four years of
admission to Ph.D. candidacy
Comprehensive Examination (Comps): Each student must pass a
written examination based on the four breadth courses for their field of
specialty to assess his or her mastery of fundamental knowledge and skills. Each
student must take the exam in the following three courses: CMSC 611
Architecture, CMSC 621 Operating Systems, and CMSC 641 Algorithms. Each student also must take the exam in one
of the following six courses: CMSC 635 Graphics, CMSC 651 Automata Theory, CMSC
655 Numerical Computation, CMSC 661 Database, CMSC 671 Artificial Intelligence,
or CMSC 681 Computer Network Architecture. The comps will be offered twice a
year (in January and August) and may be retaken only once if failed the first
time provided that the time limit (four semesters for full‑time students
and five semesters for part‑time students) is not exceeded. Any student
who fails the exam by the time limit or fails the exam twice within the time
limit will be dismissed from the graduate program. (See the Graduate Program web page for
detailed policies for comprehensive exams.)
Course requirements: Each student must satisfy the minimum course
requirements for their field of specialty (eleven courses totaling thirty-three
credits) excluding the Department’s Research Seminar, graduate research credits
prior to Ph.D. candidacy, and doctoral dissertation research credits. Students
cannot take dissertation research credits (CMSC 899) before passing the
preliminary examination.
Preliminary Examination
(Prelim): Each student must select a Dissertation
Advisor and a Dissertation Preliminary Examination Committee and must pass a
two part preliminary examination. In the first part, the student will present
and defend his or her dissertation proposal to the Prelim Committee. In the
second part, the Committee examines the student orally on his or her research
area(s) to assess his or her ability to successfully complete the proposed
research. Each full time student must
pass the prelim within one and a half years after passing the comps to remain
in the Ph.D. program (part time students will be given two and a half years to
pass the prelim).
Ph.D. Candidate: After passing the prelim and completing the
course requirements, the Graduate Program Committee recommends to the
Dissertation Research: Each student will conduct and report on a
significant original research project under the guidance of his or her
dissertation advisor. This research must be completed and defended within four
years of admission to candidacy. Students must be admitted to candidacy at
least two full sequential semesters before the date on which the doctoral
degree is to be conferred.
Residency Requirements: A minimum of three years of full‑time
graduate study or its equivalent is required. At least one year of full‑time
study must be completed at UMBC.
Facilities and Special
Resources
The Department’s computing facilities
include Sun and Silicon Graphics workstations, SGI Crimson and SPARC servers,
and high performance graphics workstations (SGI Indigo2, Onyx Reality Engine2).
The Office of Information Technology has over 400 workstations for general
student use and several high‑end machines including a Silicon Graphics
Challenge XL 20 processor system. The university’s
Centers and Laboratories
Center for Architecture for Data-Driven Information Processing (CADIP) ‑ At the CADIP center, UMBC students and faculty work alongside government scientists to solve problems related to the storage, retrieval, and analysis of large collections of documents. The center’s three focus areas include intelligent software agents, mass storage architectures, and information visualization. An important part of the center’s mission is to develop collaborations with other research institutions and to bring research results to a wide audience through courses and workshops. CADIP is sponsored by the Department of Defense.
Computer Graphics Animation &
Visualization (GAVL)
Laboratory for Advanced Information
Technology (LAIT)
Laboratory for Informational Systems
Technology (LIST)
Computer Science
The following conventions
are used for numbering graduate courses in different areas of computer science:
(x
stands for a digit in the range 0‑9)
61x, 71x, 81x: Computer Architecture and VLSI
62x, 72x, 82x:
Operating Systems
63x, 73x, 83x:
Programming Languages and Graphics
64x, 74x, 84x:
Algorithms and Software Engineering
65x, 75x, 85x: Theory of Computation and
Computational Methods
66x, 76x, 86x:
Databases
67x, 77x, 87x:
Artificial Intelligence
68x, 78x, 88x:
Computer Networks and Distributed Systems
69x, 79x, 89x:
Research and Independent Study
CMSC 603 Advanced Discrete
Structures
Credits: 3
Introduction to the
fundamental concepts and techniques of discrete mathematics that
are essential for the study of computer science. The main goal of this
course is to develop mathematical skills and sophistication for proving
theorems, solving problems, and counting and approximating values. Topics include
sets; elementary logic; numbers; functions and relations; summations;
generating functions; elementary number theory; elementary probability,
statistics, and combinatorics (e.g. Burnside’s
Lemma); introduction to algebraic systems, including groups; and applications
of these topics in computer science.
Prerequisites: MATH 152, MATH 221, and
at least one math course beyond linear algebra.
CMSC
611 Advanced Computer Architecture
Credits: 3
Memory system design, pipeline
structures, vector computers, scientific array processors, multiprocessor
architecture. Within each topic, the emphasis is on fundamental limitations:
memory bandwidth, interprocessor communication,
processing bandwidth, and synchronization.
Prerequisite: CMSC 411 or permission of
instructor.
CMSC 621 Advanced Operating
Systems
Credits: 3
A detailed study of
advanced topics in operating systems including: synchronization mechanisms,
virtual memory, deadlocks, distributed resource sharing, computer security, and
modeling of operating systems.
Prerequisite: CMSC 421 or permission of
instructor.
CMSC 625 Modeling and
Simulation of Computer Systems
Credits: 3
Performance evaluation
methods, Markovian queuing models, open networks of
queues, closed product form queuing networks, simulation and measurement of
computer systems, bench marking, and workload characterization.
Prerequisite: CMSC 411 or CMSC 421, or
permission of instructor.
CMSC 628 Introduction
to
Credits:
This course will introduce students to the techniques and research issues involved with mobile computing, which deals with access to the networked information and computation resources from wirelessly connected palmtop/laptop type devices. Topics covered deal with both networking (MAC protocols, ad-hoc routing, mobile IP) and data management (proxy based systems, mobile DBMS, mobile transactions, sensor networks and stream data) issues.
CMSC 631 Principles of
Programming Languages
Credits: 3
A comparison of three types
of modern programming languages: assertive, functional, and logic based.
Fundamental semantic methods, including operational, axiomatic, and denotational semantics and corresponding techniques for
program verification, including Hoare’s logic, Dijkstra=s predicate transformers,
and denotational methods.
Prerequisite: CMSC 331 or permission of
instructor.
CMSC 634 Computer Graphics
Credits: 3
An
introduction to the fundamentals of interactive computer graphics. Topics include graphics hardware,
line drawing, area filling; clipping, two-dimensional and three-dimensional
geometrical transforms, three-dimensional perspective viewing, hidden surface
removal, illumination, color and shading models.
CMSC 635 Advanced Computer
Graphics
Credits: 3
A study of advanced topics
in computer graphics emphasizing algorithms for display of 3D objects
including: wire frame representation, polygon mesh models, shading algorithms,
parametric representation of curves, hidden surface elimination, fractals, and
ray tracing. Other topics include:
advanced topics from the computer graphics literature, page description
languages, CORE, GKS, PHIGS, CGI, the X window system,
X window intrinsics, Motif and widget programming.
Prerequisite: CMSC 435 or permission of
instructor.
CMSC 636 Data
Visualization
Credits: 3
This
course addresses the theoretical and practical issues in creating visual
representations of large amounts of data.
It covers the core topics in data visualization: data representation, visualization toolkits,
scientific visualization, medical visualization, information visualization, and
volume rendering techniques.
Additionally, the related topics of applied human perception and
advanced display devices are introduced.
Open to computer science students with a background in computer graphics
or students in data-intensive fields who are familiar with the use of the
computer for data collection, storage, or analysis.
Prerequisite: CMSC 435, CMSC 634, or permission of instructor.
CMSC 641 Design
and Analysis of Algorithms
Credits: 3
Fundamental
algorithms, mathematical tools for analyzing algorithms, and strategies for
designing algorithms. Topics include graph algorithms (including network flow),
parallel algorithms, and algorithms for selected combinatorial tasks. Tools
include asymptotic notations, recurrences, amortized analysis, and
probabilistic analysis. Strategies include divide and conquer, greedy, dynamic
programming, time space tradeoff, and randomization. Introduction
to NP completeness.
Prerequisite: CMSC 441 or permission of
instructor.
CMSC 645 Advanced Software
Engineering
Credits: 3
Modern approaches to
software development: Requirements analysis, system design techniques, formal
description techniques, implementation, testing, debugging, metrics, human
factors, quality assurance, cost estimation, maintenance, and tools.
Prerequisite: CMSC 445 or permission of
instructor.
CMSC 651 Automata Theory
and Formal Languages
Credits: 3
Formal languages and their corresponding
classes of automata: regular languages and finite automata, context free
languages and pushdown automata, context sensitive languages and linear bounded
automata, recursively innumerable sets, and Turing machines. Also,
pumping lemmas, closure properties, and decision problems for various classes
of languages. Other sorts of
automata may be studied, including multi headed automata, probabilistic
automata, and Petri nets.
Prerequisite: CMSC 451 or permission of
instructor.
CMSC 652
Cryptography and Data Security
Credits: 3
Conventional
and public key cryptography. Selected crypt systems,
including DES and RSA. Digital signatures, pseudo‑random number
generation, cryptographic protocols, and cryptanalytic techniques. Applications of cryptography to electronic commerce.
Prerequisites: CMSC 441 and MATH 221 or
permission of instructor.
CMSC 653 Coding Theory and
Applications
Credits: 3
An
introduction to the theory of error correcting codes with an emphasis on
applications and implementations.
Prerequisite: CMSC 203 or MATH 221, or
permission of instructor.
CMSC 655 Numerical
Computations
Credits: 3
Numerical
algorithms and computations in a parallel processing environment. The
architecture of supercomputers, vectorizing compilers
and numerical algorithms for parallel computers.
Prerequisite: CMSC 411 and Math 221, or
permission of instructor.
CMSC 656 Symbolic and
Algebraic Processing
Credits: 3
Applications
and Foundations of Symbolic Algebra. Applications and examples are studied using at
least one large symbolic algebra package. Symbolic algebra combines elements of
AI, analysis of algorithms, and abstract algebra. Foundations include problems
of representation, canonical and normal forms, polynomial simplification, Buchberger s algorithm, g.c.d. in
one and several variables, panic methods, and formal methods for integration.
Prerequisites: CMSC 203 and CMSC 341 or
permission of instructor.
CMSC 657 Networks and
Combinatorial Optimizations
Credits: 3
Graph theoretic concepts, unimodular matrices, transportation problems, minimum cost
network flows, maximal flows in networks, shortest path algorithms, spanning
three problems, multi‑commodity flows and decomposition algorithms,
assignment and matching problems, computational complexity of algorithms, and
other special topics such as matroid theory and
nonlinear network minimization.
Prerequisite: CMSC 641 or permission of
instructor.
CMSC 661 Principles of Data
Base Systems
Credits: 3
Advanced
topics in the area of data base management systems: data models and their
underlying mathematical foundations, data base manipulation and query
languages, functional dependencies, physical data organization and indexing
methods, concurrency control, crash recovery, data base security, and
distributed data bases.
Prerequisite: CMSC 461 or permission of
instructor.
CMSC 665 Introduction
to Electronic Commerce
Credits:
This
course focuses on the use of electronic means to pursue business
objectives. Special emphasis is placed
on the student’s ability to do research into existing and emerging technology
and to clearly summarize and present his/her findings. The first part of the course is devoted to
enabling technologies including an introduction to business models for
e-commerce and basic infrastructure, an overview of networking technologies and
their impact on e-commerce, and discussions on database technologies and
Web-database connectivity. The second
part of the course concentrates on the issues that are not solely technical
such as trust management, privacy and personalization, selling information
products and copy protection, and the digital divide.
CMSC 666 Electronic
Commerce Technology
Credits:
This
course is designed to prepare students to be e-commerce developers. It introduces the students to the changing
and competitive landscape of e-commerce technology, products, and
solutions. The course begins with an
introduction to WWW technology, an overview of Web applications and services,
and discussions on networking technologies with the view towards mobile and
wireless commerce and object orientation and Web programming. It also covers Java language and relational
databases, database-web connectivity, inter-process communications in a
distributed environment concentrating on Java RMI and CORBA technologies,
JavaScript, dynamic HTML, XML and its applications, component programming with
JavaBeans, and WebServer servlet
architecture. The second part of the
course explores the theoretical underpinnings of decision support systems,
provides an overview of Web mining and commercial decision support products for
E-Commerce, and introduces the student to agent technology and agent-driven
e-commerce.
CMSC 671 Principles of
Artificial Intelligence
Credits: 3
A study of topics central
to artificial intelligence, including logic for problem‑solving,
intelligent search techniques, knowledge representation, inference mechanisms,
expert systems, and AI programming. Prerequisite: CMSC 471 or permission
of instructor.
CMSC 675
Introduction to Neural
Networks [3]
A comprehensive study of fundamentals of neural networks. Topics include feedforward and recurrent networks, self organizing
networks, and thermodynamic networks; supervised, unsupervised, and
reinforcement learning; and neural network application in function
approximation, pattern analysis, optimization, and associative memories.
CMSC
681 Computer Network Architecture
Credits: 3
Topics central to the design
and development of advanced computer communication networks, including
distributed and failsafe routing in large and dynamic networks, gateways and
interconnection of heterogeneous networks, flow control and congestion
avoidance techniques, network architectures, computer and communication
security, communication protocol standards, formal specification and
verification of protocols, implementation and conformance testing of protocol
standards, network partitioning and intelligent reconfiguration of networks.
Prerequisite: CMSC 481 or CMSC 621, or
permission of instructor.
CMSC 682 Networking
Technologies
Credits: 3
Topics in networking
technologies, including ISDN, ATM/B-ISDN, frame relay, SDS, routing protocols,
IP security, mobile-IP, network management, IP switching, IP/ATM integration
and wireless protocols.
Prerequisite: CMSC 681 or permission of
instructor.
CMSC 691 Special Topics in
Computer Science
Credits: 1‑3
CMSC 698 Research Project
in Computer Science
Credits: 1‑3
Individual project on a
topic in computer science. The project will result in
a scholarly paper, which
must be approved by the student=s advisor and read by another faculty member. Required of non‑thesis
M.S. students.
NOTE: May be taken for repeated credit up to a maximum of
three credits.
Prerequisite: Completion of breadth
courses or consent of the advisor.
CMSC 699 Independent Study in Computer Science
Credits: 1‑3
CMSC 711 VLSI Systems
Credits: 3
A study
of structured system design methodology in the VLSI environment. The topics include VLSI
implementation of logic, system controllers, system timing, abstractions of
VLSI circuits, algorithms for VLSI processor arrays,
highly concurrent VLSI systems, and VLSI design tools.
Prerequisite: CMSC 611 or permission of
instructor.
CMSC 721 Theory
of Processes
Credits: 3
Formal
approaches to the theory of communicating systems of processes, and logical
systems for reasoning about them. Specific systems may include Milner’s calculus of
communicating systems (CCS), Hoare’s communicating
sequential processes (CSP), and Kahn’s applications of fixpoint
theory to communicating processes.
Prerequisite: CMSC 621 or CMSC 631 or
CMSC 681, or permission of instructor.
CMSC 731 Semantics of
Programming Languages
Credits: 3
The
fundamentals of axiomatic and denotational semantics,
together with their corresponding techniques for program specification and
verification.
Axiomatic methods include Hoare=s logic and Dijkstra=s predicate transfer-MES. Denotational methods include fixpoint
theory and an introduction to the lambda calculus. Denotational
methods are used to prove the soundness of selected axiomatic proof rules.
Prerequisite: CMSC 631 or permission of
instructor.
CMSC 741 Theory of NP
Completeness
Credits: 3
An in‑depth
study of the classes P and NP, along with the concepts of reduceability
and completeness.
NP complete problems are surveyed, and reduction techniques are examined in
greater detail. An important goal is to develop skill at proving problems NP
complete.
Prerequisite: CMSC 641 or permission of
instructor.
CMSC 742 Parallel
Algorithms and Complexity
Credits: 3
Models of parallel
computation and methods for the representation of parallel algorithms are presented.
Measures of parallel complexity, and techniques for analyzing algorithms with
respect to these new measures, and parallel complexity classes, such as NC, are
studied.
Prerequisite: CMSC 641 or permission of
instructor.
CMSC 751 Theory of Computation
Credits: 3
Formal models of
computation, such as Turing machines, RAM models, and loop languages are all
shown to computer the class of partial recursive functions, leading to the
Church Turing thesis. Basic recursive function theory, including universal
functions, undecidable problems, and properties of
recursive and r.e. sets. Basic concepts of first‑order logic and their relationship to
recursion theory. Topics in advanced recursion theory may include
abstract complexity theory, oracles, the arithmetic hierarchy, and priority
methods.
Prerequisite: CMSC 651 or permission of
instructor.
CMSC 761 Theory
of Relational Databases
Credits: 3
An in‑depth
study of relational data base theory. Topics include first‑order logic, relational
calculus and algebra, query languages, query optimization, functional and multi‑valued
dependencies, normal forms, and concurrency control.
Prerequisite: CMSC 661 or permission of
instructor.
CMSC 771 Heuristics and
Knowledge Representation
Credits: 3
An in‑depth study of
two topics central to artificial intelligence: heuristics and knowledge
representation. Topics in heuristics will include the use of heuristics in
problem solving, heuristic search techniques, the admissibility of heuristic
search algorithms, performance analysis of heuristic methods, and heuristics
for game playing. Topics in knowledge representation will include predicate
calculus, frame repre-sentations, semantic nets, and inheretance.
Prerequisite: CMSC 671 or permission of
instructor.
CMSC 781 Distributed
Computing
Credits: 3
Topics central to the
design of distributed computing systems including distributed synchronization
and resource sharing, concurrency control in distributed data bases,
distributed simulations, languages for distributed computing, proof techniques
for distributed systems, and distributed operating systems.
Prerequisites: CMSC 621 and CMSC 681, or
permission of instructor.
CMSC 791 Graduate Seminar
Credits: 3
CMSC 799 Master’s Thesis
Research
Credits: 1‑6
This course is for students
in the CMSC Master’s Program engaged in master’s thesis research; may be taken
for repeated credits, but only a maximum of 6 credit hours applied toward M.S.
thesis option requirements.
Prerequisite: Open only to CMSC thesis
option students.
CMSC 800 Graduate Research
Credits: 1-6
Note: This course is for
Ph.D. students not yet admitted to Ph.D. candidacy.
Prerequisite: Open only to CS students
who have passed the Ph.D. qualifying exam.
CMSC 899 Doctoral
Dissertation Research
Credits: 1‑6
This is the dissertation
research course for Ph.D. students who have passed Ph.D. comprehensive
examination; may be taken for repeated credits (2 semesters required), but only
a maximum of 12 credit hours applied towards Ph.D. requirements.
Prerequisite: Open only to CS students
passed Ph.D. comprehensive examination
Electrical Engineering
The Department offers a graduate program leading to
the Master of Science (M.S.) and Doctor of Philosophy (Ph.D.) degrees in
Electrical Engineering. The Department provides a diversity of courses
offerings and research interests. Further, our interactions with the medical
and dental schools at UMAB, with the Mathematics and Physics Departments at
UMBC, and with the Electrical Engineering Department at UMCP, encompass a broad
spectrum of interdisciplinary instruction and research as well as strictly
electrical engineering instruction and research.
Areas of specialization in EE include:
Communications (random processes, detection
and estimation theory, information theory, source and channel coding,
communication theory, wireline and wireless
communication, optical fiber communications, data compression, and applications
of adaptive signal processing in communication).
Microelectronics
(solid‑state electronics, semiconductor devices and processing
technology, semiconductor optoelectronics, compound semiconductor electronics,
and integrated circuits).
Photonics
(electromagnetic theory, quantum electronics, lasers, photonics, non‑linear
optics, fiber optic communications, and ultrafast
optics).
Signal Processing (signal and linear system theory,
digital signal processing, adaptive signal processing, remote sensing, image
processing, computer vision, speech processing, neural networks, pattern
recognition, spectral and time-frequency analysis, and biomedical signal
processing and imaging).
Program
Admission & Financial Assistance
General Policy: When seeking admission to the graduate
program, applicants must satisfy all entrance requirements of the
Minimum requirements for admission to the graduate
program in Electrical Engineering are a B.S. degree from an ABET accredited
undergraduate program in computer or electrical engineering with a GPA of “B+”
or higher. Individuals whose records indicate strong potential for successful
pursuit of the M.S. or Ph.D. 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 B.A. degree in the sciences). Students
whose degrees are not in electrical or computer engineering will generally be
required to take courses to make up deficiencies in their backgrounds.
Students may apply for admission to either the M.S.
or Ph.D. program. However, admission to the Ph.D. program is highly selective,
and only the student with an exceptional background will be accepted. Financial
aid will be preferentially given to Ph.D. students. New students will be
assigned an academic advisor who can provide advice on choice of courses,
degree requirements, and other important matters during the first year. By the
end of the first year, M.S. and Ph.D. students should seek a faculty member to
serve as research advisor for their M.S. thesis or Ph.D. dissertation research.
Application
deadlines are specified by the
|
Fall semester ‑ June 1 Spring semester ‑ November 1 International students and permanent
residents who are attending or have attended a foreign school: Fall semester ‑ January 1 Spring semester ‑
June 1 of the prior calendar year. |
The
application review process will begin by February 1 for admission to the Fall semester and by October 1 for admission to the
following Spring semester. It is the policy of the Department to admit students
based solely on their academic and research performance.
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) and graduate research assistantships (RAs).
Preference for TAs is given to first year Ph.D. applicants and to those
students whose academic background (and work experience) best matches the
anticipated TA duties. Graduate RAs are often available to students actively
engaged in funded research of the faculty and are awarded and renewed subject
to availability of funds and satisfactory research progress. Students are
encouraged to also apply directly to nationally awarded fellowship programs for
financial support.
Requirements for the Master of Science (M.S.)
Degree: Within five years of admission, the student
must earn a minimum of thirty (30) credit hours under the thesis option or
thirty-three (33) credit hours under the non‑thesis option. The student
must satisfy the GPA and course requirements for his/her area of specialty and
attend the Department’s Research Seminar (ENEE 608). Each student must complete
either a thesis or a scholarly paper.
Thesis Option: The program of study in the student’s
specialty area requires a minimum of eight graduate‑level courses (24
credit hours) and six credit hours of thesis (ENEE 799). The thesis must be
defended with an oral exam and accepted by the student’s M.S. thesis committee.
A bound copy of the thesis must be submitted both to the department and the
Non-Thesis Option: The program of study in
the student’s specialty area requires a minimum of ten graduate‑level
courses (30 credit hours) and three credit hours of graduate project (ENEE 698)
resulting in a scholarly paper that must be approved by the student’s research
advisor and another faculty member.
Required Courses: Breadth courses provide a uniform background
and a minimum breadth requirement for graduate students. There are four breadth
courses required for each specialty area, and they constitute the core course
requirement. The number of breadth courses is intentionally designed to be
small to offer maximum course planning flexibility to professors and graduate
students.
For
students choosing the Microelectronics
and Photonics specialty area, the following describes the MSEE degree
requirements for both thesis and non-thesis options:
MSEE Thesis Option
(30 credits) Requirements for Microelectronics and Photonics
Students must take the
following:
ENEE 630
ENEE 631 Semiconductor
Devices
ENEE 680 Electromagnetic Theory I
ENEE 683 Lasers
ENEE 799 Thesis (6 credits)
Must take two of the
following elective courses (6 credits) from Technical Electives in Microelectronics and Photonics:
ENEE 632 Integrated Circuits
ENEE 634 Microwave Devices and Circuits
ENEE 635 Intro Optical Communications
ENEE 636 Wireless Communications
ENEE 684 Intro Photonics
ENEE 685 Intro Communication Networks
ENEE 735 Photonics Integrated Circuits
ENEE 736 Intro Optical Communication Systems
ENEE 737 Semiconductor Device Processing Techniques
ENEE 738 Characteristics of Semiconductor Optoelectronics
ENEE 785 Topics in Optical Networks
ENEE 788 Topics in Photonics
And must take two 600/700
level advisor-approved courses.
A grade of “B” (3.0 GPA) or better is required in
all “must take” courses and a minimum of 3.0 GPA overall.
MSEE
Non-Thesis Option (33 credits) Requirements for
Microelectronics and Photonics
Students must take the
following:
ENEE 630
ENEE 631 Semiconductor Devices
ENEE 680 Electromagnetic Theory
I
ENEE 683 Lasers
ENEE 698 Graduate Project w/scholarly paper (3 credits)
Must take four of the
following elective courses (12 credits) from Microelectronics and Photonics area:
ENEE 632 Integrated Circuits
ENEE 634 Microwave Devices and Circuits
ENEE 635 Intro Optical Communications
ENEE 636 Wireless Communications
ENEE 684 Intro Photonics
ENEE 685 Intro Communication Networks
ENEE 735 Photonics Integrated Circuits
ENEE 736 Intro Optical Communication Systems
ENEE 737 Semiconductor Device Processing Techniques
ENEE 738 Characteristics of Semiconductor Optoelectronics
ENEE 785 Topics in Optical Networks
ENEE 788 Topics in Photonics
And must take two 600/700
level advisor-approved courses (can include one 400-level course).
A grade of “B” (3.0 GPA) or
better is required in all “must take” courses and a minimum of 3.0 GPA overall.
For
students choosing the Communications and
Signal Processing specialty area, the following describes the MSEE degree requirements
for both thesis and non-thesis options:
MSEE Thesis Option
(30 credits) Requirements for
Communications and
Signal Processing
Students must take the
following:
ENEE 601 Signal and Linear
Systems Theory
ENEE 620 Probability and Random Processes
ENEE 621 Detection
and Estimation Theory
ENEE 799 Thesis (6 credits)
Must take three of the
following elective courses (9 credits):
ENEE 610 Digital Signal Processing
ENEE
611Adaptive Signal Processing
ENEE 612 Digital Image Processing
ENEE 622 Information Theory
ENEE 623 Communication Theory
ENEE 624 Error Correcting Codes
And must take two 600/700
level advisor-approved courses.
A grade of “B” (3.0 GPA) or
better is required in all “must take” courses and a minimum of 3.0 GPA overall.
MSEE Non-Thesis
Option (33 credits) Requirements for
Communications and
Signal Processing
Students must take the
following:
ENEE 601 Signal and Linear
Systems Theory
ENEE 610 Digital Signal Processing
ENEE 620 Probability and
Random Processes
ENEE 621 Detection
and Estimation Theory
ENEE 698 Graduate Project w/scholarly paper (3 credits)
Must take four of the
following elective courses (12 credits):
ENEE 611Adaptive Signal Processing
ENEE 612 Digital Image Processing
ENEE 622 Information Theory
ENEE 623 Communication Theory
ENEE 624 Error Correcting Codes
ENEE 63X
ENEE 68X
And must take two 600/700
level advisor-approved courses (can include one 400-level course).
A grade of “B” (3.0 GPA) or
better is required in all “must take” courses and a minimum of 3.0 GPA overall.
Research Courses: Research courses provide
the course credits for the student’s research activities, e.g., ENEE 699 Independent
Study, ENEE 698 Graduate Project, and ENEE 799 M.S. Thesis.
Graduate Seminar: Each student must attend the Departmental
Graduate Seminar course (ENEE 608) for one semester.
Comprehensive Examination: The Comprehensive
Examination is not required for M.S. students. M.S. students planning to pursue
a Ph.D. degree may choose to take this exam before completing the M.S. degree
requirements.
M.S. Thesis: M.S. students choosing the thesis option must
undertake ENEE 799 Master’s Thesis which is supervised by a faculty member as
the Thesis Advisor. Upon completion of the thesis research and document, the
thesis must be defended in a public presentation. The required six credits of
ENEE 799 must be taken over two or more semesters.
Scholarly Paper: M.S. students choosing
the non-thesis option must undertake ENEE 698 Graduate Project which is
supervised by a faculty member as the Graduate Project and Scholarly Paper
Advisor. Upon completion of the graduate project and document, the scholarly
paper must be approved by the advisor and a second faculty member, and a copy
submitted to the department.
Transfer Credits: No more than six credits
may be transferred from another university. Credit transfer must be approved by
the Director of the Graduate program.
Requirements for the Doctor of Philosophy
Requirements for the Doctor
of Philosophy (Ph.D.)
Degree: Each specialty area (the
Communications and Signal Processing Area, the Microelectronics and Photonics
Area) sets its course requirements for Ph.D. students in that specialty. The
Department’s minimum requirement is eleven (11) courses excluding graduate
seminar participation, graduate research credits prior to Ph.D. candidacy, and
doctoral dissertation research credits (ENEE 899). Four of these eleven courses
must be taken at UMBC (at least 2 of these courses have to be ENEE courses).
The doctoral dissertation must be an original and substantive contribution to
knowledge in the student’s major area and 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 area. Students are expected to formally present their results
at conferences and/or in technical journals related to their specialty area.
The Ph.D. student must:
1. Pass the written comprehensive exam
(comps) within four semesters of entrance to the program (five semesters for
part‑time students). The comps are based on the core courses for their
area of specialty to assess his or her mastery of fundamental knowledge and
skills. Microelectronics and Photonics students must take the comps in ENEE
630, ENEE 631, ENEE 680, and ENEE 683. Communications and Signal Processing
students must take the comps in ENEE 601, ENEE 620, ENEE 621, and ENEE 622. The
comps will be offered twice a year between the semesters (typically in January
and August). Each Ph.D. student must pass the EE comps within the first four
semesters of admission to the Ph.D. Program (first five semesters for part-time
students). All students must pass the comps within two attempts or will be
dismissed from the Ph.D. Program. (See the Graduate Program web page for
detailed policies for comprehensive exams.)
2. Develop and defend a doctoral dissertation proposal
(preliminary exam) and be admitted to Ph.D. candidacy within four years of
entrance to the program (five years for part‑time students).
3. Complete all Ph.D. requirements for their area of specialty
within four years after admission to Ph.D. candidacy (see requirements below).
Ph.D. Requirements for Photonics and Microelectronics
11 courses (total number of courses required beyond B.S.
degree)
Students must take the
following:
ENEE 630
ENEE 631 Semiconductor Devices
ENEE 680 Electromagnetic Theory I
ENEE 683 Lasers
Must take four electives
from the list below at least two of them being 700-level:
ENEE 632 Integrated
Circuits
ENEE 634 Microwave Devices
and Circuits
ENEE 635 Intro Optical
Communications
ENEE 636 Wireless
Communications
ENEE 684 Intro Photonics
ENEE 685 Intro
Communication Networks
ENEE 735 Photonics
Integrated Circuits
ENEE 736 Intro Optical
Communication Systems
ENEE 737 Semiconductor
Device Processing Techniques
ENEE 738 Characteristics of
Semiconductor Optoelectronics
ENEE 785 Topics in Optical
Networks
ENEE 788 Topics in
Photonics
And must
take any three additional advisor-approved graduate courses.
A grade of “B” (3.0 GPA) or better is required in
all courses and a minimum of 3.33 GPA overall (including the transfer courses).
Comprehensive Exam
Courses
ENEE 630
ENEE 631 Semiconductor
Devices
ENEE 680 Electromagnetic Theory I
ENEE 683 Lasers
Ph.D. Requirements for Communications and Signal Processing
12 courses (total number of courses required beyond B.S.
degree)
Student must take the
following:
ENEE
601 Signal and
Linear Systems Theory
ENEE 620 Probability and Random Processes
ENEE 621 Detection and Estimation Theory
ENEE 622 Information Theory
Must take two of the
following courses:
ENEE 610 Digital Signal Processing
ENEE 611 Adaptive Signal Processing
ENEE 612 Digital Image Processing
ENEE 623 Communication Theory
ENEE 624 Error Correcting Codes
And must
take:
At least one MATH/STAT course
(600-level or higher).
At least two additional ENEE courses (700-level or
higher)
Any three additional advisor-approved
graduate courses.
A grade of
“B” (3.0 GPA) or better is required in all courses and a minimum of 3.33
GPA overall (including the transfer courses).
Comprehensive Exam
Courses
ENEE 601 Signal and Linear
Systems Theory
ENEE 620 Probability and Random Processes
ENEE 621 Detection and
Estimation Theory
ENEE 622 Information Theory
Graduate Seminar: Each Ph.D. student must attend the
Department’s Graduate Seminar course (ENEE 608) for two semesters usually
during his/her first year.
Course Requirements: Each student must satisfy the minimum course
requirements for their area of specialty excluding the Department’s Graduate
Seminar, graduate research credits prior to Ph.D. candidacy (ENEE 800, ENEE
899), and doctoral dissertation research credits (ENEE 899). Students may take
up to six credits of dissertation research credits (ENEE 899) after passing the
comprehensive examination and before being admitted to Ph.D. candidacy.
Preliminary Examination (Prelim): Each student must select a Dissertation
Advisor and a Dissertation Preliminary Examination Committee and must pass a
two-part preliminary examination. In the first part, the student will present
and defend his or her dissertation proposal to the Prelim Committee. In the
second part, the Committee examines the student orally on his or her proposal
and research area(s) to assess his or her ability to successfully complete the
proposed research. Each full-time
student must pass the prelim within one and a half years after passing the
comps to remain in the Ph.D. program (part-time students will be given two and
a half years to pass the prelim).
Ph.D. Candidacy: After passing the comps and prelim and
completing the course requirements, the Graduate Program Committee recommends
to the
Dissertation Research: Each student will conduct and report on a
significant original research project under the guidance of his or her
dissertation advisor. This research must be completed and defended within four
years after admission to Ph.D. candidacy. Students must be admitted to
candidacy at least two full sequential semesters before the date on which the
doctoral degree is to be conferred.
Residency Requirements: A minimum of three years of full‑time
graduate study or its equivalent is required. At least one year of full‑time
study must be completed at UMBC.
Electrical Engineering:
The following conventions are used for numbering
graduate courses in different areas of electrical engineering (x stands for a
digit in the range 0‑9):
60x:
61x,
71x, 81x: Signal Processing
62x,
72x, 82x: Communications
63x,
73x, 83x: Microelectronics
64x,
65x, 74x, 75x, 84x, 85x: Computer Engineering
68x,
78x, 88x: Photonics
69x, 79x, 800, 89x: Research and Independent Study
ENEE 601 Signal and Linear Systems
Theory
Credits: 3
This is a first semester, required, graduate course
for electrical engineering (EE) majors that covers the fundamentals of signal
and linear systems theory. The course will address both continuous-time and
discrete-time representations and both time-invariant and time-variant systems.
Topics covered include: (1) Fundamental linear space and matrix concepts; (2)
Signal representations, properties, transforms, and sampling; (3) System
representations, properties, and transforms; and (4) New transforms and
representations, e.g., joint-domain transforms and representations. The goal of
this course is to provide the beginning EE graduate student with the foundations
and tools of signal and linear system theory, particularly the time-variant
case in both continuous-time and discrete-time, necessary for subsequent
courses in the overall electrical engineering program, in general, and the
communications and signal processing subprogram, in particular, and for
conducting research in related areas.
Co-requisite: ENEE 620.
ENEE 608 Graduate Seminar
Credits: 0
This course exposes the
graduate student in EE to the current research in areas of interest to the
department’s faculty and students. The speakers are usually researchers
outside, as well as inside, the department and university. On occasion,
speakers may be faculty members or advanced students. There are no credits for
this course, which meets once a week, but all graduate students are required to
attend (one semester for M.S. students and two semesters for Ph.D. students).
ENEE 610
Digital Signal Processing
Credits: 3
This is a first year graduate course for
communication and signal processing majors in electrical engineering (EE) that
covers the fundamentals of digital signal processing (DSP). The goal of this
course is to provide the first year EE graduate student with the foundations
and tools to understand, design, and implement DSP systems, in both hardware
and software. MATLAB and SystemView will be the
primary vehicles to provide the student with hands-on DSP design and simulation
experience. The student will also acquire an understanding of DSP hardware
basics and architecture. Topics covered include: (1) A/D-D/A conversion and
quantization, number representations, and finite wordlength
effects; (2) FIR, IIR, and lattice filter structures, block diagram and
equivalent structures; (3) Multirate DSP and filterbanks; (4) Digital filter design methods and verification;
(5) DSP hardware architecture; and (6) DSP simulation/laboratory experiences.
Prerequisites: ENEE 601,
620, or their equivalent, or permission of instructor.
ENEE 611 Adaptive Signal Processing
Credits: 3
Fundamentals of adaptive
filters and associated algorithms: Mean-square error and least squares
approaches; steepest-descent algorithm; the least-mean-square adaptive filters,
recursive least-squares adaptive filters, frequency-domain and subband adaptive filters, and unsupervised adaptive filters;
analysis of these adaptive filters, and discussion of selected applications.
Prerequisites: ENEE 601 or 610, and 620, or consent of
instructor.
ENEE 612 Digital Image Processing
Credits: 3
Principles of two-dimensional
processing of image data: fundamentals of 2D signal processing, image
transforms, image enhancement, image filtering and restoration, color image
processing, image coding and wavelet quantization, image thresholding
and segmentation, image interpretation and recognition, applications of image
processing.
Co-requisite: ENEE 620; Prerequisites:
MATLAB, or consent of instructor.
ENEE 618 Special Topics in Signal Processing
Credits: 3
ENEE 618 comprises special
topic courses in signal processing that reflect the research interests of the
faculty and graduate students. A specific offering under this title is
designated by a letter appended to this course and is generally not offered
every year.
Prerequisite:
depends on offering or permission of instructor.
ENEE 620 Probability and Random Processes
Credits: 3
Fundamentals of probability
theory and random processes for electrical engineering applications and
research: set and measure theory and probability spaces; discrete and
continuous random variables and random vectors; probability density and
distribution functions, and probability measures; expectation, moments, and
character-stic functions; conditional expectation and
conditional random variables, limit theorems and convergence concepts; random
processes (stationary/non-stationary, ergodic, point
processes, Gaussian, Markov, and second‑order); applications to
communications and signal processing.
Prerequisite: Undergraduate probability or consent of
instructor.
ENEE 621 Detection and Estimation Theory I
Credits: 3
Fundamentals of detection
and estimation theory for statistical signal processing applications: theory of
hypothesis testing (binary, multiple, and composite hypotheses, and Bayesian, Neyman Pearson, and minimax
approaches); theory of signal detection (discrete and continuous time signals;
deterministic and random signals; white Gaussian noise, general independent
noise, and special classes of dependent noise, e.g., colored Gaussian noise;
signal design and representations); theory of signal parameter estimation:
Minimum variance unbiased (MVU) estimation, Cramer-Rao
lower bound, general MVU estimation, linear models, maximum likelihood
estimation, least squares, general Bayesian estimators (minimum mean square
error and maximum a posteriori estimators), linear Bayesian estimators (Wiener
filters), and Kalman filters.
Prerequisite: ENEE 620 or consent of instructor.
ENEE 622 Information Theory
Credits: 3
Prerequisites: Strong grasp of basic probability theory
ENEE 623 Communication Theory
Credits: 3
A review of the
Prerequisites: ENEE 601, ENEE 621, and ENEE 622
ENEE 624 Error Correcting Codes
Credits: 3
Fundamentals of error
correction coding theory: linear block and trellis codes, decoder structures,
random and burst error detection and correction techniques, encoding/decoding
performance and bounds, concatenated codes and interleaving structures, and
turbo and LDPC codes and iterative
decoding concepts.
Prerequisites: ENEE 620 and 622 or 623, or consent of
instructor.
ENEE 625 Data Compression
Credits: 3
Principles and techniques
of data compression: review of source coding theory; lossless data compression
techniques such as Huffman coding, bit-plane coding, predictive coding,
arithmetic coding, and LZW coding; and lossy data
compression techniques such as transform coding, wavelet transform coding,
scalar quantitation, vector quantitation,
predictive coding, and sub-band coding.
Prerequisites: ENEE 620 and 622, or consent of instructor.
ENEE 628 Special Topics in Communications
Credits: 3
ENEE 628 comprises special
topic courses in communications that reflect the research interests of the
faculty and graduate students. A specific offering under this title is
designated by a letter appended to this course and is generally not offered
every year. Prerequisite: depends on offering or permission of instructor.
ENEE 630 Solid‑state Electronics
Credits: 3
Fundamentals
of solid‑state physics for the microelectronics field: review of quantum mechanics and statistical
mechanics, crystal lattices, reciprocal lattices, dynamics of lattices,
classical concepts of electron transport, band theory of electrons,
semiconductors, and excess carriers in semiconductors.
Prerequisite: Consent of instructor.
ENEE 631 Semiconductor Devices
Credits: 3
Principles
of semiconductor device operation: review of semiconductor physics, p‑n
junction diodes, bipolar transistors, metal semiconductor contacts, JFETs and
MESFETs, and MIS and MOSFET
structures.
Prerequisite: ENEE 630, or consent of
instructor.
ENEE 632 Integrated Circuits
Credits: 3
Fundamentals
of bipolar and MOS analog and digital integrated circuit techniques: basic IC
structure and fabrication, passive components, bipolar transistors and diode, characterictics matching, temperature compensation, output
stages, frequency analysis, OpAmps., voltage
regulators, multiplers, PLLs,
MOS digital and analog circuits, memories, A/D converters, CMOS logic circuits.
Prerequisite: ENEE 630, 631 or consent
of instructor.
ENEE 634 Microwave Device and Circuit Design
Credits: 3
Basic
concept and knowledge of microwave devices and integrated circuits for wireless
communications, transmission lines and lumped elements, impedance matching
networks, hybrids, couplers, filters, multiplexers, oscillators, amplifiers,
detectors, and mixers, microwave tubes or frequency multiplers,
MMIC, and laboratory.
Prerequisites: ENEE 681 or consent of
instructor.
ENEE 635 Introduction to Optical Communications
Credits: 3
Introduction
to basic principles of optical communications: Optical fibers, transmitters,
receivers, optical system design and performance, optical amplifiers, and
multi-channel communication systems.
Prerequisite: ENEE 630, or consent of
instructor.
ENEE 636 Introduction to Wireless
Communications
Credits: 3
Introduction to wireless
communication systems, the cellular concept, mobil
radio propagation: large-scale path loss and small-scale fading and multipath, modulation techniques, equalization, diversity,
compression, multi-access techniques, wireless networking and wireless systems
and standards.
Prerequisite: consent of instructor.
ENEE 680
Electromagnetic Theory I
Credits: 3
Fundamentals of dynamics in
electromagnetic theory: theoretical analysis of Maxwell’s equations,
Electrodynamics, plane waves, waveguides, dispersion, radiating systems, and
diffraction.
Prerequisite: Consent of instructor.
ENEE 683 Lasers
Credits: 3
Introduction to basic
theory of lasers: Introduction to quantum mechanics and time dependent
perturbation theory, interaction of radiation and matter, stimulated and
spontaneous emissions, rate equations, laser amplification and oscillation,
noise in lasers and laser amplifiers, semiconductor lasers,
Prerequisites: ENEE 680, or consent of instructor.
ENEE 684 Introduction to Photonics
Credits 3
This course covers the fundamentals of photonics and
their applications. Subjects include crystal and polarization optics, Jones
calculus and Stokes parameters, polarization mode dispersion, fiber optics,
planar waveguide optics, electro-optics, acousto-optics,
second and third order nonlinear susceptibilities, second harmonic generation,
sum-frequency generation, parametric down-conversion and oscillation,
self-focusing, self- and cross-phase modulation, optical solitions,
four-wave mixing, Raman scattering, Brillouin
scattering, phase conjugation, photorefractive optics, photo detectors and
noise characteristics.
Prerequisite: ENEE 680.
ENEE 685/CMPE 485 Introduction to Communication Networks
Credits: 3
The fundamentals of
communication and computer networking, 7-layer OSI model, review of queuing
models, transmissions, WDM, circuit and packet switching, data link and medium
access technologies, X.25, Frame Relays, ISDN, xDSL,
cable modem, SONET, the network layer, ATM, TCP/IP, routing techniques, the
transport and application layers, quality of Services (QoS).
Prerequisite: consent of
instructor.
ENEE 688 Special Topics in Photonics
Credits: 3
ENEE 688 comprises special
topics courses in photonics and optical technologies that reflect the research
interests of the faculty and graduate students. A specific offering under this
title is designated by a letter appended to this course and is generally not
offered every year.
Prerequisite: depends on offering or permission of instructor
ENEE 698 Research Project in Electrical Engineering
Credits: 1‑3
Individual
project on topic in electrical engineering. The project will result in
a scholarly paper, which must be approved by the student’s advisor and read by
another faculty member. Required of non‑thesis option
M.S. students. NOTE: May be taken for repeated credit up to a
maximum of three credits.
Prerequisite: Completion of core courses, or consent of
instructor.
ENEE 699 Independent Study
Credits: 1‑3
Independent
study of topics in electrical engineering.
Prerequisite: Consent of instructor.
ENEE 710 Digital Speech Processing
Credits: 3
Fundamentals and techniques
for the digital processing of speech: digital signal processing concepts
review, speech production models, characteristics of the speech signal, time
domain speech analysis, linear predictive coding (LPC), homomorphic
speech processing, speech enhancement, speech recognition, speech coding, and
speech synthesis.
Prerequisites: ENEE 610 and 611, or consent of instructor.
ENEE 711 Neural Networks in Signal Processing
Credits: 3
Fundamentals and
characteristics of artificial neural network paradigms and their properties
in association, learning,
generalization, and self organization: introduction and survey of various
neural network models and paradigms, multilayer perceptron
and the radial basis function networks, sum-of-squares and
information-theoretic cost functions, different learning procedures (gradient
optimization, conjugate gradients, Newton, etc.), learning and generalization
properties, applications in communications and biomedical signal processing,
and comparisons with linear adaptive signal processing theory and techniques.
Prerequisites: ENEE 620 or consent of instructor.
ENEE 712 Pattern Recognition
Credits: 3
Principles of statistical
pattern recognition; hypothesis testing and decision theory; parametric estimation
(Bayesian estimation, maximum likelihood estimation, Gaussian mixture
analysis); non-parametric estimation (nearest neighbor rule and Pazen’s window method); density approximation; linear discriminant functions; feature extraction and selection; feature
optimization; neural networks (single-layer perceptrons,
multi-layer neural networks); and applications in pattern classification.
Prerequisites: ENEE 612, 620, and 621,
or consent of instructor.
ENEE 718 Topics in Signal Processing
Credits: 3
ENEE 718 comprises advanced
topic courses in signal processing that reflect the research interests of the
faculty and their Ph.D. students. A specific offering under this title,
designated by a letter appended to this course number, is generally not offered
every year.
Prerequisites: (Depends on offering) Consent of instructor.
ENEE 721 Statistical Signal Processing
Credits: 3
Advanced concepts of signal
detection and estimation theory with applications: sequential detection; non parametric
and robust detection concepts; small signal and small sample size concepts and
performance; estimation techniques for smoothing, filtering, and prediction;
recursive, interactive, and extended Kalman filter
and other state estimation techniques and their performance; robust estimation
concepts; general nonlinear filtering and approximately optimal simplified
filters; and discussion of current applications in communications and
statistical signal processing.
Prerequisites: ENEE 620 and 621, or consent of instructor.
ENEE 723 Multiuser Communications
Credits: 3
Digital signaling over
bandwidth constrained channels and channels with distortion: digital
communications over fading multipath channels,
inter-symbol interference and its effects, adaptive equalization, combined
coding and modulation techniques (e.g., trellis coded modulation), and spread
spectrum techniques. Discussion of selective applications.
Prerequisites: ENEE 620, 621, and 623, or consent of instructor.
ENEE 728 Topics in Communications
Credits: 3
ENEE 728 comprises advanced
topic courses in communications that reflect the research interests of the
faculty and their Ph.D. students. A specific offering under this title,
designated by a letter appended to this course number, is generally not offered
every year.
Prerequisite: (Depends on offering) Consent of instructor.
ENEE 737 Semiconductor Device Processing Techniques
Credits: 3
Introduction to basic semiconductor device
processing techniques: etching, photolithography, metalization,
and device characterization. Laboratory exercises will complement the lectures
and demonstrate the principles.
Prerequisites: ENEE 630 and 631, or consent of instructor.
ENEE 738 Characteristics of Semiconductor Optoelectronics
Credits: 3
Introduction
to current semiconductor optoelectronic devices and survey of new research
results: review of semi-conductor physics and device characteristics; optical
receiver concepts such as photoconductors, metal‑semiconductor concepts,
MSM, pin, receiver design, and APD; waveguide concepts such as waveguide
devices, waveguide modes, waveguide couplers, EO effects and modulation,
periodic waveguides, polarization devices, waveguide filters, and BPM; and LED
amplifier and laser concepts such as edge/surface emitting, optical gain,
traveling wave amplifiers, FP, DFB, DBR, QW lasers, active filters, small
signal modulation, modelocking, line width, and
noise.
Prerequisites: ENEE 630, 631, 681, 682, and 683, or consent of
instructor.
ENEE 785 Topics in Optical Networks
Credits: 3
This is an
interdisciplinary course to address the issues of importance in constructing
high‑speed optical networks. It covers the current networks for both
telecoms and datacoms. Network layers, circuit
switching and packet‑switching principle and technologies are described.
Depending on the instructor, technologies related to the physical layer of the
system, protocols and traffic and network control will be covered in more
detail. Projects are required for all students.
Prerequisite: (Depends on offering) Consent of instructor.
ENEE 788
Topics in Photonics
Credits: 3
ENEE 788 comprises advanced topic courses in
photonics that reflect the research interests of the faculty and their Ph.D.
students. A specific offering under this title, designated by a letter appended
to this course number, is generally not offered every year.
Prerequisite: (Depends on offering) Consent of instructor.
ENEE 799
Master’s Thesis Research
Credits: 1‑5
This course is for MSEE students engaged in
master’s thesis research; may be taken for repeated credits, but only a maximum
of 6 credit hours applied toward M.S. thesis option requirements. Must be taken
over at least two (2)
semesters.
Prerequisite: Open only to MSEE thesis
option students.
ENEE 800 Graduate Research
Credits: 1‑6
This course is for Ph.D. students not yet admitted
to Ph.D. Candidacy, and can be taken for repeat credit.
Prerequisite: Open only to EE students
who have passed the Ph.D. qualifying exam.
ENEE 899 Doctoral Dissertation Research
Credits: 1‑6
Ph.D. students must take
this course over at least two semesters. Only a maximum of twelve (12) credit
hours can be applied towards the Ph.D. requirements, and only six (6) credit hours
can be taken before admission to Ph.D. candidacy.
Prerequisite: Open only to EE students who have passed the
Ph.D. qualifying exam.
Computer Engineering
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 Ph.D. program emphasizes research as a major element of its
degree requirements.
Fields
of specialization in Computer Engineering supported within the department
include:
·
VLSI
design and testing including mixed signal analog and digital design
·
Systems
hardware/software co-design and testing
·
Computer
networks
·
Digital
signal processing
General Policies
When
seeking admission to the graduate program, applicants must satisfy all entrance
requirements of the
Application deadlines
Note
that the application deadlines are specified by the
|
Fall semester ‑ June 1
Spring semester ‑ November 1
International students and permanent residents who are attending or
have attended a foreign school:
Fall semester ‑
January 1
Spring semester ‑ June 1 of the prior academic
year. |
The
application review process will begin by February 1 for admission to the Fall semester and by October 1 for admission to the
following Spring semester. It is the policy of the department to admit students
based solely on their academic and research performance.
Students
may apply for admission to either the M.S. or Ph.D. program. However, admission
to the Ph.D. program is highly selective, and only the student with an
exceptional background will be accepted. Financial aid will be preferentially
given to the Ph.D. students. New students will be assigned an academic advisor
who can provide advice on choice of courses, degree requirements, and other
important matters during the first year. By the end of the first year, M.S. and
Ph.D. students should seek a faculty member to serve as research advisor for
their M.S. thesis or Ph.D. dissertation research.
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) and
graduate research assistantships (RAs). Preference for TAs is given to first
year Ph.D. applicants and to those students whose academic background (and work
experience) best matches the anticipated TA duties. Graduate RAs are often
available to students actively engaged in funded research of the faculty and
are awarded and renewed subject to availability of funds and satisfactory
research progress. Students are encouraged to also apply directly to nationally
awarded fellowship programs for financial support.
Prerequisites of M.S. and Ph.D. Degree Programs
An
applicant to the graduate program in Computer Engineering is expected to have a
strong background in computer engineering, computer science, and mathematics
courses. This includes the Calculus course series, Linear Algebra, Differential
Equations, and Probability and Statistics in mathematics. In addition,
applicants are expected to have taken courses equivalent to the following
Computer Engineering, Computer Science, and Electrical Engineering courses at
UMBC:
|
CMPE 310 Systems Design and Programming CMPE 312 Principles of Digital Design CMPE 314 Electronic Circuits CMSC 413 Principles of VLSI Design CMSC 341 Data Structures CMSC 411 Computer
Architecture CMSC 421 Principles of Operating Systems ENEE 206 Basic Circuit Theory |
Course Listings:
Breadth
Courses:
|
CMSC 611 Computer
Architecture CMPE 640 Advanced VLSI Design CMPE 650 Digital Systems Design And one of the following: CMPE 642 Principles of Mixed Signal
Design
CMPE 645 Computer Arithmetic Algos &
Implementations CMPE 646 VLSI Design
Verification and Testing ENEE 610 Digital Signal Processing |
CMPE
Electives:
|
CMPE 641 Advanced VLSI Design II CMPE 645 Computer Arithmetic Algos & Implementations CMPE 646 VLSI Design
Verification and Testing Note that new elective CMPE courses will be
developed |
New CMPE Electives (to be developed as new faculty are hired):
|
CMPE 642 Principles of Mixed Signal
Design CMPE 643 Low Power Design CMPE 644 High Level Synthesis CMPE 645 CAD Algorithms CMPE 651 Parallel Architectures CMPE 652 Fault
Tolerant Computing CMPE 653 Robotics CMPE 725 Contemporary Issues |
New faculty hires in Computer Engineering will
develop most of the new courses above in addition to adding special topics
courses in their areas of research. Courses will be developed over the next
several years, coinciding with the faculty buildup in the department.
CMSC Electives:
|
CMSC 621 Operating Systems CMSC 625 Modeling and Simulation of Computer Systems CMSC 641 Design and Analysis of
Algorithms CMSC 652 Cryptography and Data Security CMSC 653 Coding Theory and Applications CMSC 655 Numerical Computations CMSC 681 Computer Network Architecture CMSC 682 Networking Technologies CMSC 691 Special Topics in Computer Science CMSC 742 Parallel Algorithms CMSC 781 Distributed Computing |
|
ENEE 611 Adaptive Signal Processing ENEE 612 Digital Image Processing ENEE 620 Probability and Random Processes ENEE 622 Information Theory ENEE 623 Communication Theory I ENEE 624 Error Correcting Codes ENEE 625 Data Compression ENEE 630 ENEE 631 Semiconductor Devices ENEE 680 Electromagnetic Theory I ENEE 681 Electromagnetic Theory II ENEE 683 Lasers ENEE 684 Introduction to Photonics ENEE 723 Multiuser Communications ENEE 737 Semiconductor Device Processing Techniques ENEE 738 Characteristics of Semiconductor Optoelectronics ENEE 785 Topics in Optical Networks |
Most
graduate courses currently offered in the CSEE department, as well as new courses
that might be developed in the future, are available as electives for the
Computer Engineering program.
Requirements for the Master of Science (M.S.)
Within
five years of admission, the student must earn a minimum of 30 credit hours
with the thesis option or 33 credit hours with the non-thesis (project) option
(there is no course-only option; all students MUST do either a thesis or a
project). The student must satisfy the GPA and course requirements for their
field of specialty and attend the department's Graduate Seminar (CMPE 608). The
thesis option in the student's field
requires a minimum of eight
graduate-level courses and six credit hours of thesis, CMPE 799. The thesis
must be defended with an oral exam and accepted with the approval of the
student's M.S. thesis committee. The committee must consist of at least three
graduate faculty within the department. The non-thesis (project) option in the
student's field requires a minimum of ten
graduate-level courses and three credit hours of CMPE 698 Research Project work
resulting in a scholarly paper which must be approved by the advisor and an
additional reader.
Required Courses
Breadth Courses
Each
student must take four breadth courses, distributed as given in the Course
Listing section and receive a grade of B or better in each course.
Additional Courses
Beyond
the four breadth courses and the six credits of CMPE 799 (M.S. Thesis), a
minimum of twelve additional course
credits is required for students who choose the thesis option. For students who
choose the non-thesis (project) option, the minimum number of additional course
credits (beyond the four breadth courses and three CMPE 698 credits) is eighteen. Under either the M.S.
non-thesis (project) or M.S. thesis options, students must take two courses from the CMPE Electives given in the Course
Listing section. The remaining course requirements are specified as
follows.
M.S. Non-Thesis (Project)
Two
courses must be selected from each of the CMSC
Electives and ENEE Electives
given in the Course Listing section.
M.S. Thesis
One
course must be selected from the CMSC
Electives and the other course from the ENEE
Electives given in the Course Listing section. Any student
may undertake a master's thesis supervised by a faculty member as the thesis
advisor. M.S. degree candidates undertake a thesis (for six credits) which
demonstrates a tangible research component. Upon completion of the thesis
research, the thesis must be defended in a public presentation.
Research Courses
Research
courses provide the course credits for the student's research activities, e.g.,
independent study, graduate project, scholarly paper, and M.S. thesis.
Graduate Seminar
Each
student must attend the Graduate Seminar course (CMPE 608) for one semester.
Transfer Credits
No
more than six credits may be transferred from another university. Credit
transfers and/or exceptions to the six credit transfer limit must be approved
by the Graduate Program Director and the Associate Dean of the
Requirements for the Doctor
of Philosophy (Ph.D.)
Each
field of specialty sets its course requirements for Ph.D. students in that
field. The department's minimum requirement is eleven courses (beyond Bachelor's degree). The Ph.D. student must
spend the equivalent of at least three years of full-time residency with at
least one year on the UMBC campus. 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.
The
Ph.D. student must:
(1)
Pass
the written comprehensive exam within four semesters of entrance to the program
(five semesters for part-time students)
(2)
Develop
and defend a doctoral dissertation proposal and be admitted to Ph.D. candidacy
within four years of entrance to the program
(3)
Complete
all Ph.D. requirements for their field of specialty within four years of
admission to Ph.D. candidacy
Comprehensive Examination
Each
student must pass a written examination based on the material covered in the
four breadth courses. The comprehensive examinations are offered twice a year
and may be
retaken once if failed the first time provided the time
limit (four semesters for full-time students and five semesters for part-time
students) is not exceeded. Any student who fails the exam twice will be
dismissed from the graduate program. (See the department’s graduate program web
page at www.csee.umbc.edu/~graddir/CSEE/ for detailed policies for
comprehensive exams.)
Graduate Seminar
Each
student must attend the department's Graduate Seminar course (CMPE 608) for one
semester usually during the first year.
Course Requirements
Each
student must satisfy the minimum course requirements for their field of
specialty (typically eleven courses
totaling 33 credits) excluding the department's Graduate Seminar, graduate
research credits prior to Ph.D. candidacy, and doctoral dissertation research
credits. Four of the eleven courses must be taken from the Breadth Courses as specified in the Course Listing section.
The
remaining elective courses must be distributed such that a student takes at
least two courses from each of the CMPE Electives, CMSC Electives, and ENEE
Electives given in the Course Listing section.
Students
cannot take dissertation research credits (CMPE 899) before passing the
preliminary examination.
Preliminary Examination
Each
student must select a dissertation advisor and a dissertation preliminary
examination committee. Each student must pass a two part preliminary
examination. In the first part, the student will present and defend his or her
dissertation proposal to the committee. In the second part, the committee examines
the student orally on his or her research area(s) to assess his or her ability
to successfully complete the proposed research. Each full-time student must
pass the preliminary examination within one and a half years after passing the
comprehensive exams to remain in the Ph.D. program (part-time students will be
given two and a half years to pass the preliminary examination).
Ph.D. Candidacy
After
passing the preliminary examination and completing the course requirements, the
graduate program committee recommends to the
Dissertation Research
Each
student will conduct and report on a significant original research project
under the guidance of his or her dissertation advisor. This research must be
completed and defended within four years of admission to candidacy. Ph.D.
candidates take at least 16 dissertation credits, and the dissertation must
demonstrate a significant contribution to the state of the art in the topic
selected. The Ph.D. dissertation committee must include four graduate faculty
members from the CSEE department and one external member. Students must be
admitted to candidacy at least two full sequential semesters before the date on
which the doctoral degree is to be conferred.
Residency Requirements
A
minimum of three years of full-time graduate study or its equivalent is
required. At least one year of full-time study must be completed at UMBC.
Facilities and Special Resources
The
department's computer engineering facilities include two dedicated computer
engineering laboratories that provide computers and test and measurement
equipment. The department also provides two dedicated servers that allow
students to use commercial design software. The Office of Information Technology
(OIT) has over 400 workstations for general student use and several high-end
computing systems.
Program Admission Requirements
Applicants must apply separately to the
graduate programs in Computer Engineering, Computer Science, and Electrical Engineering.
Admission processes for the M.S. degree program and the Ph.D. degree program
are separate. Applications are not processed until all documents and fees have
been received.
Computer Engineering:
The following conventions are used for numbering
graduate courses in different areas of computer engineering (x stands for a
digit in the range 0‑9):
CMPE 640 Advanced VLSI Design
Credits: 3
This course introduces the CMOS VLSI design process and focuses
on design at the circuit and physical levels. Students design, implement,
fabricate and test basic logic gates and other VLSI structures such as adders
and multipliers using computer aided design tools and laboratory test and measurement
equipment. Basic layout and simulation techniques are covered in addition to
CMOS processing technology, MOS transistor theory, performance estimation, CMOS design styles, VLSI structures and timing
issues. The Verilog hardware description language is
used in the laboratories.
CMPE 641 Advanced VLSI Design
II
Credits: 3
This course is focused on the design, implementation, fabrication and testing of a large VLSI chip. Advanced CMOS
design topics are covered including BiCMOS and
dynamic logic circuits. system level design entities
such as ALUs, Register Files, Functional Units,
Controllers, and clock and power distribution schemes. The Verilog
high-level description language and high-level synthesis tools are also covered
as well as Design-For-Testability design issues. Students work in groups of
four to design, implement and test a CMOS implementation of a system level
design entity such as a microprocessor.
Prerequisites: CMPE 640.
CMPE 642 Principles of Mixed
Signal Design
Credits: 3
This course covers both the practical and theoretical aspects
of mixed-signal design -- the integration of digital and analog circuitry with
computer systems, and digital signal processing systems. The course content
includes discussion of oversampling techniques,
delta-sigma data converters, custom analog and digital filter design, design
with submicron CMOS processes.
Prerequisites: CMPE 640.
CMPE 645 Computer Arithmetic
Algorithms and Implementations
Credits: 3
Introduction to arithmetic, unconventional
fixed-radix number systems, sequential algorithms for multiplication and
division, binary floating point numbers, fast addition and multiplication, fast
division and square root, evaluation of elementary functions
(polynomial/rational function methods as well as CORDIC), logarithmic and
residue number representations. Other topics are covered in articles from
current literature in the area.
CMPE 646
VLSI Design Verification and Test
Credits
: 3
This course covers the design verification and testing processes
applied to VLSI digital integrated circuits. Design and hardware level testing
and failure analysis processes are examined in detail. Hardware testing
concepts covered include fault modeling, fault simulation, automatic test
pattern generation (ATPG), functional test, logic and parametric testing
techniques. Built-in self test, design for testability, sequential test
generation issues are also examined. Commercial computer aided verification and
ATPG tools are used to generate tests on existing designs.
Prerequisites: CMPE 640
CMPE 650 Digital Systems
Design
Credits: 3
This
course covers practical and theoretical aspects necessary to design high-speed
digital systems. Topics include transmission line theory, cross-talk and
non-ideal transmission line effects on signal quality and timing, impact of
packages, vias and connectors on signal integrity.
Other issues covered include non-ideal return paths, simultaneous switching
noise, power delivery, buffer modeling and digital timing analysis. Linux device
driver design and implementation will also be covered.
CMPE 698 Project in Computer Science
Credits: 1-3
Individual
project on a topic in computer engineering. The project will result in
a scholarly paper which must be approved by the student's advisor and read by
another faculty member. Note: Required of non-thesis M.S. students. May be repeated up to a maximum of three credits.
Prerequisite: Completion of breadth courses or consent of the
advisor.
CMPE 699 Independent Study
Credits: 1-3
Independent Study of topics
in computer engineering.
Prerequisite: Consent of instructor.
CMPE 799 Master's Thesis Research
Credits: 1-6
This course is for students
in the CMPE master's program engaged in master's thesis research. Note: May be taken
for repeated credits but only a maximum of six credit hours applied toward M.S.
thesis option requirements.
Prerequisite: Open only to CMPE thesis-option students.
CMPE 899 Doctoral Dissertation Research
Credits: 1-6
This dissertation research
course is for Ph.D. students who have passed the Ph.D. preliminary examination
or will be taking the preliminary examination in the semester they are enrolled
in this course. Note: May be taken for repeated credits (two semesters
required) but only a maximum of 12 credit hours applied toward Ph.D.
requirements.
Department of
Computer Science Electrical
Engineering (CSEE)
CSEE Faculty
Charles Nicholas, Chair
Gary M. Carter, Graduate Program Director, Electrical Engineering
Krishna M. Sivalingam, Graduate Program Director, Computer Science
James Plusquellic,
Graduate Program Director, Computer Engineering
Faculty
Professors
Gary
M. Carter,
Professor; Ph.D., MIT. Optoelectronics, diode lasers, nonlinear
optics, coherent optical communications.
Chein‑I Chang, Professor; Ph.D.,
Yung Jui (Ray) Chen, Professor; Ph.D.,
Fow‑Sen Choa, Professor; Ph.D., SUNY
Tim Finin, Professor; Ph.D.,
Samuel
Lomonaco, Professor; Ph.D.,
Curtis
R. Menyuk, Professor; Ph.D., UCLA. Light propagation, optical
fibers, nonlinear phenomena.
Joel
M. Morris,
Professor; Ph.D., Johns
Charles Nicholas, Professor; Ph.D., The
Sergei Nirenburg,
Professor; Ph.D.; The
Zary Segall,
Professor; D.Sc., Technion.
Validation and testing of network quality of service, wearable computers,
wearable information systems, mobile wireless computing
John
Pinkston,
Professor and Chair; Ph.D., MIT. Coding Theory, Information
Security, Electronic Commerce, and Antennas.