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Computer Science

For more information contact

Director of Graduate Studies
Department of Computer Science
Duke University
Box 90129
Durham, NC 27708-0129
(919) 660-6500

Email: dgs@cs.duke.edu

http://www.cs.duke.edu

General Information

Degree offered:
M.S., Ph.D.
Faculty working with students:
37
Students:
80
Students receiving Financial Aid:
100% of Ph.D. students (none available for MS)
Test required:
GRE General. TOEFL or IELTS required of all international applicants according to Graduate School guidellines.
Application Deadlines

Program Description

Part time study available: Consult department

The Department of Computer Science offers programs leading to the M.S. and Ph.D. degrees in areas such as algorithms, architecture, artificial intelligence, scientific computing, and systems. The M.S. program consists of a coursework--only option (30 credits) or a thesis or project option, which requires the supervision of a faculty advisor and an oral defense.  The Ph.D. program consists of coursework and a sequence of research milestones culminating in a doctoral dissertation. The M.S. requires advanced courses in the area of concentration, two courses from a related field, and two approved electives.  The Ph.D. program requires advanced courses in the area of concentration as well as a breadth requirement, satisfied by earning qualifying credits on four out of six subjects.  All entering Ph.D. students participate in a special seminar course (COMPSCI 701) that introduces them to the discipline and profession of computer science.  A student entering graduate study in computer science should have suitable undergraduate preparation in mathematics and computer science. Students should consult the departmental document Graduate Degree Requirements of the Computer Science Department for a full description of degree requirements.

Current research interests of faculty span broad areas of algorithms, architecture, artificial intelligence, scientific computing, and systems. They also include specific fields such as architectures for emerging technologies, fault-tolerant architectures, memory and storage systems, operating systems, networking, distributed systems, mobile and wireless systems, database and data-intensive systems, social/collaboration systems, computational geometry and topology, DNA nanoscience and DNA-based computing, computational biology, machine learning, vision, robotics, computational economics, numerical analysis, and mathematical foundations of computer science.

Statistics

Spring Application

No