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Department of Computer Engineering
Lee and Seymour Graff Professor: Ruth E. Davis
“Computing sits at the crossroads among the central processes of applied mathematics, science, and engineering. The three processes have equal and fundamental importance in the discipline, which uniquely blends theory, abstraction, and design.”
The most successful graduates in the field of computing are those who understand computers as systems—not just the design of hardware or software, but also the relationships and interdependencies between them and the underlying theory of computation.
The department offers a variety of degree and certificate programs, including courses that cover the breadth of the discipline, from the engineering aspects of hardware and software design to the underlying theory of computation.
Students are required to meet with their advisors to define and file a program of study during their first quarter. In general, no credit is allowed for courses that duplicate prior coursework, including courses listed as degree requirements. Students should arrange adjustment of these requirements with their academic advisor when they file their program of study.
With the prior written consent of the advisor, master’s students may take a maximum of 12 units of coursework for graduate credit from selected senior-level undergraduate courses.
Master of Science in Computer Science and Engineering (MSCSE)
The SCU courses listed above are considered undergraduate-level and may not be used to satisfy the requirements for the M.S. in Computer Engineering. However, students who have satisfied item 6 above, but who have never studied numerical analysis, may use AMTH 220/221 as electives; students who have satisfied item 6 above, but who have never studied linear algebra, may use AMTH 245/246 as electives. Laboratory components are not required for the above courses.
Please Note: Students wishing to do a thesis (COEN 497) should consult with their academic advisor regarding a modification of these requirements.
Master of Science in Software Engineering (MSSE)
The program consists of the SCU Engineering core, a software engineering core, a set of software engineering electives, and a capstone project. Students are allowed to sample courses across diverse software disciplines, including databases, networks, parallel and distributed systems, graphical user interfaces, artificial intelligence, and computer languages. Students must work with their advisor to select 15 units of appropriate software engineering electives. The capstone project comprises three consecutive terms of effort and provides an opportunity for students to apply their technical breadth and the core engineering principles toward the development of a complex, team-oriented software project. Ideally, projects will involve collaboration with industry. The capstone project integrates the engineering knowledge acquired in the core courses with the technical breadth acquired in the diverse electives. Thus, students must complete all requirements of the core prior to registering for the first capstone project course. They must also complete 6 units of electives prior to registering for the second 2 units of the capstone course, COEN 485, to ensure the project teams have the appropriate blend of technical background and engineering knowledge.
Doctor of Philosophy in Computer Science and Engineering
Time Limit for Completing Degree
Engineer’s Degree in Computer Science and Engineering
Admission to the program will generally be granted to those students who demonstrate superior ability in meeting the requirements for their master’s degree. Normally, the master’s degree is earned in the same field as that in which the engineer’s degree is sought. Students who have earned a master’s degree from Santa Clara University must file a new application (by the deadline) to continue work toward the engineer’s degree. A program of study for the engineer’s degree should be developed with the assistance of an advisor and submitted during the first term of enrollment.
Certificate programs are designed to provide intensive background in a narrow area at the graduate level. At roughly one-third of the units of a master’s degree program, the certificate is designed to be completed in a much shorter period of time. These certificate programs are appropriate for students working in industry who wish to enhance their skills in an area in which they already have some background knowledge.
For more specific application and admissions information, please consult the website.
Students must receive a minimum grade of C in each course and an overall GPA of 3.0 or better to earn a certificate of completion.
Continuation for a Master’s Degree: All Santa Clara University courses applied to the completion of a certificate program earn graduate credit that may also be applied toward a graduate degree. Students who wish to continue for such a degree must submit a separate application and satisfy all normal admission requirements. The general GRE test requirement for graduate admission to the master’s degree will be waived for students who complete a certificate program with a GPA of 3.5 or better.
Certificate in Software Engineering
This certificate program places an emphasis on methodologies used in the development of large, complex software. The program is appropriate for anyone who is developing new software, maintaining existing software, or is the technical head of a software development project. In addition to the general requirements, students must have two years of industrial experience in software development and prior coursework in data structures and analysis of algorithms, software engineering, discrete mathematics, and predicate logic.
Required Courses (10 units)
Elective Courses (Select any 6 units; other courses may be considered if approved in advance)
Certificate in Information Assurance
The Advanced Studies in Information Assurance Certificate program provides education in information assurance to working professionals in engineering and engineering management. Applicants are expected to have previous coursework in Operating Systems and Networks. In addition, applicants must complete all courses in Group 1, and 8 units from Group 2 and additional courses should be chosen from Group 2 or Group 3 for a total of 16 units.
Group 1: Required Courses (4 units)
Group 2: Select enough courses for 8 units
Group 3: Elective Courses
Certificate in Networking
This certificate program is appropriate for working professionals in computer engineering, network engineering, and engineering management, and places an emphasis on the fundamentals and recent developments in computer networking. Students who complete the program may pursue a professional career in computer networking, with the ability to understand, analyze, design, implement, validate, and maintain networked systems.
Applicants must have completed an accredited bachelor’s degree program in Computer Science, Computer Engineering, Electrical Engineering, Mathematics or an equivalent field with a strong academic record, and are expected to have prior coursework in data structures, analysis of algorithms, software engineering and operating systems.
Program Requirements: Students must complete a total of 16 units of prescribed coursework with a minimum GPA of 3.0 and a grade of C or better in each course. Certificate requirements substantially equivalent to other coursework completed within the last five years must be replaced by electives approved by the faculty in charge of networking.
Required Courses (8 units)
Additional Courses (8 units) from:
The ASIC Testing Laboratory supports research conducted by graduate students from the departments of Electrical Engineering and Computer Engineering. Computer-aided testing packages from industry and the public domain are used in projects such as fault modeling and analysis. Projects include design for Test on RTL-level for digital and mixed signal circuits and design for reliability based on the defect-based testing.
The Digital Systems Laboratory (operated jointly with the Department of Electrical Engineering) provides complete facilities for experiments and projects ranging in complexity from a few digital integrated circuits to FPGA-based designs. The laboratory also includes a variety of development systems to support embedded systems and digital signal processing.
TheGreen Computing Laboratory is devoted to energy-efficient computing, i.e., the study and analysis of energy consumption in operating systems and networks and the development of energy-aware software.
The Multimedia Compression Laboratory supports research in video coding (compression and decompression).
The Wireless Networks Laboratory is shared by Computer Engineering and Electrical Engineering. The lab carries out research projects on the lower three layers of wireless networks. Current projects include: 1) Efficient scheduling of user traffic in cellular networks using smart antennas, 2) Algorithms for turn-key base stations in cellular networks, and 3) Changes to the MAC protocol in 802.11 based ad-hoc networks.
The Sustainable Computing Laboratory is dedicated to research in systems software and data storage technologies. The projects it supports focus on durable, scalable, and efficient solutions to computing problems, and the application of systems software technologies to broader sustainability problems.
The Software Engineering Research Laboratory is a dedicated facility not only for the support of various research activities aimed at developing engineering techniques and tools that help produce and validate high-quality software, but also for developing applications using leading-edge technologies.
The Parallel Processing Laboratory pursues research in fundamental problems in parallel processing, multi-core CPUs, and many-core GPUs programming and parallelizing compilers.
The Intelligent Information Systems (I2S) Laboratory is devoted to the theory, design and implementation of intelligent systems to manage, retrieve, mine, and use information. The work of I2S covers a wide range of areas including web search, information retrieval, cloud computing, social media analysis, AI, machine learning, data mining, recommendation systems, NLP, computer vision, biomedical informatics, computational politics, and computer security