Department of Computer Science
ANSELMO A. LASTRA, Chair
Stanley Ahalt (82) Director of the Renaissance Computing Institute (RENCI); Signal, Image, and Video Processing; High-Performance Scientific and Industrial Computing; Pattern Recognition Applied to National Security Problems; High-Productivity, Domain-Specific Languages
James Anderson (62) Real-Time Systems, Distributed and Concurrent Algorithms, Multicore Computing, Operating Systems
Sanjoy K. Baruah (78) Scheduling Theory, Real-Time and Safety-Critical System Design, Computer Networks, Resource Allocation and Sharing in Distributed Computing Environments
Gary Bishop (39) Hardware and Software for Man-Machine Interaction, Assistive Technology, 3D Interactive Computer Graphics, Virtual Environments, Image-Based Rendering
Frederick P. Brooks Jr. (9) 3D Interactive Computer Graphics, Human-Computer Interaction, Virtual Worlds, Computer Architecture, the Design Process
Prasun Dewan (63) User Interfaces, Distributed Collaboration, Software Engineering Environments, Mobile Computing, Access Control
Henry Fuchs (11) Virtual Environments, Telepresence, Future Office Environments, 3D Medical Imaging, Computer Vision and Robotics
Kevin Jeffay (40) Computer Networking, Operating Systems, Real-Time Systems, Multimedia Networking, Performance Evaluation
Anselmo A. Lastra (52) Interactive 3D Computer Graphics, Hardware Architectures for Computer Graphics
Ming C. Lin (72) Physically Based and Geometric Modeling, Applied Computational Geometry, Robotics, Distributed Interactive Simulation, Virtual Environments, Algorithm Analysis, Many-Core Computing
Dinesh Manocha (58) Interactive Computer Graphics, Geometric and Solid Modeling, Robotics Motion Planning, Many-Core Algorithms
Stephen M. Pizer (6) Image Display and Analysis, Medical Imaging, Human and Computer Vision, Graphics
David A. Plaisted (28) Mechanical Theorem Proving, Term Rewriting Systems, Logic Programming, Algorithms
Jan F. Prins (33) High Performance Computing: Parallel Algorithms, Programming Languages, Compilers, and Architectures; Scientific Computing with Focus on Computational Biology and Bioinformatics
Michael K. Reiter (95) Computer and Network Security, Distributed Systems, Applied Cryptography
Jack S. Snoeyink (79) Computational Geometry, Algorithms for Geographical Information Systems and Structural Biology, Geometric Modeling and Computation, Algorithms and Data Structures, Theory of Computation
David Stotts (59) Computer-Supported Cooperative Work, Especially Collaborative User Interfaces; Software Engineering, Design Patterns and Formal Methods; Hypermedia and Web Technology
Wei Wang (90) Bioinformatics and Computational Biology, Data Mining, Database Systems
Kye S. Hedlund (22) Software Engineering, Computer-Aided Design Tools
Jasleen Kaur (88) Design and Analysis of Networks and Distributed Systems, High-Speed Congestion Control, Resource Management, Internet Measurements, and Transport Protocols
Ketan Mayer-Patel (80) Multimedia Systems, Networking, Multicast Applications
Leonard McMillan (87) Computational Biology, Genetics, Genomics, Bioinformatics, Information Visualization, Data-Driven Modeling, Image Processing, Imaging Technologies, Computer Graphics
Fabian Monrose (91) Computer and Network Security, Biometrics and User Authentication
Montek Singh (84), High-Performance and Low-Power Digital Systems, Asynchronous and Mixed-Timing Circuits and Systems, VLSI CAD Tools, Energy-Efficient Graphics Hardware, Applications to Computer Security, Emerging Computing Technologies
Ron Alterovitz (99) Medical Robotics, Motion Planning, Physically Based Simulation, Assistive Robotics, Medical Image Analysis
Jan-Michael Frahm (97) Structure From Motion, Camera Self-Calibration, Camera Sensor Systems, Multi-Camera Systems, Multi-View Stereo, Robust Estimation, Fast Tracking of Salient Features in Images and Video, Computer Vision, Active Vision for Model Improvement, Markerless Augmented Reality
Vladimir Jojic (124) Bioinformatics, Computational Biology, Machine Learning
Marc Niethammer (98) Quantitative Image Analysis, Shape Analysis, Image Segmentation, Deformable Registration, Image-Based Estimation Methods
Marc Pollefeys (89) Computer Vision, Image-Based Modeling and Rendering, Image and Video Analysis, Multi-View Geometry
Diane Pozefsky (93) Software Engineering and Environments, Computer Education, Serious Games Design and Development, Social, Legal and Ethical Issues Concerning Information Technology
F. Donelson Smith (42) Computer Networks, Operating Systems, Distributed Systems, Multimedia
Russell M. Taylor II (69) 3D Interactive Computer Graphics, Virtual Worlds, Distributed Computing, Scientific Visualization, Human-Computer Interaction
Gregory F. Welch (71) Human Motion Tracking Systems, 3D Telepresence, Projector-Based Graphics, Computer Vision and View Synthesis, Medical Applications of Computers
Research Associate Professor
Mary C. Whitton (81) Developing and Evaluating Technology for Virtual and Augmented Reality Systems, Virtual Locomotion, Tools for Serious Games
Research Assistant Professors
Jay Aikat (126) Experimental Methods and Models in Networking Research and Education, Measurement and Modeling of Internet Traffic, Protocol Benchmarking, Internet Traffic Generation, Wireless Networks, Congestion Control and Active Queue Management
Martin Styner (94) Medical Image Processing and Analysis Including Anatomical Structure and Tissue Segmentation, Morphometry Using Shape Analysis, Modeling and Atlas Building, Intra and Inter-Modality Registration
Tessa Joseph Nicholas (86) New Media Arts and Poetics, Digital Communities, Digital-Age Ethics
Timothy L. Quigg (83) Management and Organization Dynamics in Research-Intensive Organizations, Intellectual Property Rights and Creative Methods for Capturing and Commercializing University Technology
Leandra Vicci (35) Information Processing Hardware: Theory, Practice, Systems, and Applications; Computer-Integrated Magnetic Force Systems; Wave Optics, Tracking and Imaging; Electricity and Magnetism; Low Reynolds Number Fluid Dynamics; Biophysical Models of Mitotic Spindles; Quantum Theory
Professor of the Practice
Rob Fowler (110) High-Performance Computing
Guido Gerig (75) Image Analysis, Shape-Based Object Recognition, 3D Object Representation and Quantitative Analysis, Medical Image Processing
M. Gail Jones, (113) Science Education, Gender and Science, High-Stakes Assessment, Nanotechnology Education, Haptics and Learning
J. Stephen Marron (114) Smoothing Methods for Curve Estimation
John McHugh (129) Computer and Network Security
John Poulton (120) Graphics Architectures, VLSI-Based System Design, Design Tools, Rapid System Prototyping
Julian Rosenman (112) Computer Graphics for Treatment of Cancer Patients, Contrast Enhancement for X-Rays
Diane H. Sonnenwald (106) Collaboration among Multidisciplinary, Cross-Organizational Teams, Collaboration, Human Information Behavior, Digital Libraries
Richard Superfine (115) Condensed Matter Physics, Biophysics, Microscopy
Alexander Tropsha (111) Computer-Assisted Drug Design, Computational Toxicology, Cheminformatics, Structural Bioinformatics
Sean Washburn (116) Condensed Matter Physics, Materials Science
Adjunct Associate Professors
Stephen R. Aylward (109) Computer-Aided Diagnosis, Computer-Aided Surgical Planning, Statistical Pattern Recognition, Image Processing, Neural Networks
Chris Healey (105) Computer Graphics, Scientific Visualization, Perception and Cognitive Vision, Color, Texture, Databases, and Computational Geometry
Steven E. Molnar (108) Architectures for Real-Time Computer Graphics, VLSI-Based System Design, Parallel Rendering Algorithms
Frank Mueller (121) Embedded/Real-Time Systems, Parallel/Distributed Systems, Compilers/Code Optimization
Dinggang Shen (104) Medical Image Analysis, Computer Vision, Pattern Recognition
Adjunct Assistant Professors
Derek Chiang (127)
Brad Davis (107) Image Analysis, Shape Analysis, Image Processing, Statistical Methods in Nonlinear Spaces, Medical Applications, Visualization, Software Engineering
Shawn Gomez (102) Bioinformatics, Computational Biology, Systems Biology
Hye-Chung Kum (103) Social Welfare Intelligence and Informatics, Health Informatics, Government Informatics, Data Mining, KDD (Knowledge Discovery in Databases), Government Administrative Data
Svetlana Lazebnik (96) Object Recognition and Scene Interpretation; Internet Photo Collections; Reconstruction of 3D Objects from Photos/Video; Machine Learning Techniques for Visual Recognition Problems; Clustering and Vector Quantization; Nonlinear Dimensionality Reduction and Manifold Learning
Yun Li (128)
Ipek Oguz (125) Medical Image Analysis
William Valdar (130)
Adjunct Research Professors
Nicholas England (119) Systems Architectures for Graphics and Imaging, Scientific Visualization, Volume Rendering, Interactive Surface Modeling
Turner Whitted (122)
Adjunct Research Associate Professor
Lars Nyland (117) High Performance Computing, Hardware Systems, Computer Graphics and Image Analysis, Geometric Modeling and Computation
Adjunct Research Assistant Professor
Mark Foskey (118) Medical Image Analysis, Especially in Cancer Therapy, Geometric Computation
John H. Halton
Gyula A. Magů
John B. Smith
Donald F. Stanat
Stephen F. Weiss
Research Professor Emeritus
William V. Wright
The Department of Computer Science at UNCĖChapel Hill, established in 1964, was one of the first independent computer science departments in the United States. Its primary missions are research and graduate and undergraduate teaching. Research particularly emphasizes
• bioinformatics and computational biology
• computer architecture
• computer graphics
• computer-supported collaborative work
• computer vision
• databases and data mining
• geometric computing
• high-performance computing
• human-computer interaction
• medical image analysis
• real-time systems
• software engineering
The M.S. and Ph.D. curricula are oriented toward the design and application of real computer systems and toward that portion of theory that guides and supports practice. The Ph.D. program prepares teachers and researchers for positions with universities, government research laboratories and industry. Academic employment ranges from four-year colleges, where teaching is the primary focus, to positions at major research universities. The M.S. program prepares highly competent and broadly skilled practitioners. A majority of the masterís graduates work in industry, in companies ranging from small start-up operations to government labs and large research and development corporations.
Most of the departmentís approximately 150 graduate students are full-time. Students contribute to nearly every aspect of the departmentís operation. In addition to taking a wide variety of courses, they participate in groundbreaking research, teach, attend research group meetings, and can serve on committees that affect all aspects of life in the department.
The Computer Science Students Association sponsors both professional and social events and represents the students in departmental matters. Its president is a voting member at faculty meetings.
The Department of Computer Science is housed in two adjacent buildings, the Frederick P. Brooks Jr. Computer Science Building and J. Carlyle Sitterson Hall. These two buildings are connected by hallways on all floors so that they function as a single, larger building.
The Brooks Building was dedicated in 2008 and named for the departmentís founding chair, Frederick P. Brooks Jr. It opened up 32,000 square feet of new research space, offices, and classrooms. These include a 50-seat classroom; the Stephen F. Weiss Seminar Room, with seating for 20 around a table; the Registrarís classroom, with theater seating for 80; and the Faculty Conference Room, which seats 50 at tiers of curved desktops. Meetings or discussion groups take place in
the chairís conference room and in five smaller meeting areas, each
with projectors. Perhaps the most striking area of the building is the new noise-controlled graphics lab, which is divided into three areas
by floor-to-ceiling blackout curtains for light and sound suppression.
It has 11-foot ceilings and a unistrut mounting grid to mount hardware as needed.
Sitterson Hall, which opened in 1987 and is named for former University Chancellor J. Carlyle Sitterson, provides 74,000 square feet of sophisticated, state-of-the-art research facilities and office space. It is organized in "clusters" to create research communities featuring shared laboratories and open conference areas to facilitate interaction among students and faculty. Included are the 60-seat C. Hugh Holman video teleclassroom, named for the former provost and dean of the Graduate School who was instrumental in establishing this department; a 125-seat auditorium; the Lib Moore Jones Classroom, named for the departmentís first secretary; a reading room; and various research laboratories, conference areas, and study areas.
Graduate students have access to all of the departmentís research and teaching facilities, including specialized research laboratories for graphics and image processing, computer building and design, and collaborative, distributed, and parallel systems. The laboratories, offices, conference areas, and classrooms are bound together by the departmentís fully integrated, distributed computing environment.
General Computing Environment
The departmentís computing environment includes over 1,000 computers, ranging from older systems used for generating network traffic for simulated Internet experiments to state-of-the-art workstations and clusters for graphics- and compute-intensive research. Departmental servers provide compute service, disk space, email, CVS (version control software), Web service, database services, backups, and many other services. All systems are integrated by means of high-speed networks and are supported by a highly skilled technical staff that provides a consistent computing environment throughout the department. Most students are assigned to a two- or three-person office, though we also have one larger office that can hold 11 students. Each student is assigned a computer, with computer assignments based on the studentsí research or teaching assignments and their seniority within the department. In addition to the departmental servers and office systems, our research laboratories contain a wide variety of specialized equipment and facilities.
General computing systems include 800+ Intel-based computers plus about 50 Macintosh systems. The departmentís most powerful system is the Biomedical Analysis and Simulation Supercomputer (BASS, pronounced like base), which consists of 452 CPUs tightly coupled to each other and to 180 GPU computing processors that function as image and geometry calculation accelerators, providing the equivalent computing power of more than 13,000 processors for image-intensive applications.
Our systems primarily run the Windows 7 operating system, with some still running Windows XP and a smaller number of systems, including many of the servers, running Red Hat Linux. We have other flavors of Linux as well, including some desktops and servers running Ubuntu and Fedora. In addition, a large number of network research systems run FreeBSD. We use the AFS file system for central file storage. Languages most commonly used include J++, C++, Java, and C. Document preparation is usually accomplished with standard applications on PC systems. Our extensive software holdings are continually evolving.
Students have access to the entire University library system, which includes a major academic affairs library and numerous satellite libraries containing more than 6,000,000 books and periodicals, and access to libraries at North Carolina State, Duke, and North Carolina Central universities with a unified online searching capability. The Brauer Library, located in adjacent Phillips Hall, is a satellite library with extensive holdings in computer science, mathematics, operations research, physics, and statistics.
A flexible course of study for the M.S. and Ph.D. degrees focuses on areas of choice and accommodates differences in studentsí backgrounds. The two degree programs share a basic distribution requirement chosen from theory and formal thinking, systems and hardware, and applications subject areas. The Ph.D. program includes work in specialized areas, preparation for teaching, and active involvement in advanced research.
Master of Science
An M.S. candidate must earn 30 semester hours of credit in courses numbered 400 or higher, of which up to six hours may be transferred from another institution or graduate program, and of which 18 hours must be completed in the Computer Science Department. A candidate must also satisfy the program product requirement and must demonstrate the ability to write a professional-quality technical document. A comprehensive exam (written or oral) is required for degree completion. For more in-depth information see www.cs.unc.edu/cms/academics/graduate-programs/mastersreqofficial.
Doctor of Philosophy
Admission to the doctoral program is by a vote of the department faculty and is determined by performance on the preliminary research presentation and exam, course grades, admissions information, accomplishment on assistantships, and other testimony from the faculty. Admission is normally considered following the research presentation and exam. Students who have been major contributors to a paper submitted to a well-known, refereed conference or journal may apply for a waiver of the admissions exam. There is no credit hour requirement for the Ph.D. program, but a Ph.D. candidate must complete courses to satisfy the distribution requirement and any needed background preparation, and must write a comprehensive paper. A candidate must also satisfy the program product requirement, teach a course, participate in the technical communication seminar, pass an oral examination in the proposed dissertation area, and submit and defend a dissertation that presents an original contribution to knowledge. The normal time needed to complete the degree by a full-time student with an assistantship is five years. For more in-depth information see www.cs.unc.edu/cms/academics/graduate-programs/doctoralreqofficial.
Admissions and Financial Aid
Admission to the department is highly competitive and preference is given to applicants who are solidly prepared. Although the department welcomes promising students from all disciplines, entering students must have a substantial background in both mathematics and computer science. This background normally includes at least six semester courses in mathematics and six in computer science. Students who are admitted but who have not completed all the requirements must complete them after admission. For more in-depth information on the admissions process see www.cs.unc.edu/cms/admissions/admissions-graduate-programs/admitreq and gradschool.unc.edu/admissions/.
Sponsorship. Because of the large number of applicants, the departmentís faculty members are unable to provide individual assessments of an applicantís chances for admission. Applicants cannot improve their chances of admission by finding a faculty sponsor within the department, because all admissions decisions are made by a faculty committee that reviews all applications, ranks the applicants by overall merit, and makes decisions on admission and financial support based on the application material submitted. Students are assigned to specific research projects just prior to the start of each semester, after faculty members and students have had an opportunity to meet and to discuss their interests.
Deadlines. Applicants for fall admission are encouraged to submit all application materials, complete with a personal statement, all transcripts and recommendations to The Graduate School by January 1. To ensure meeting that deadline, applicants are encouraged to take the Graduate Record Examination (GRE) no later than December 1. Early submission of applications is encouraged. International applicants should complete their applications earlier to allow time for processing financial and visa documents.
For more information, send electronic mail to email@example.com. Interested persons are encouraged to visit the departmentís Web site, www.cs.unc.edu.
Courses for Graduate and Advanced Undergraduate Students
401 Foundation of Programming (4). A first formal course in computer programming is required. Advanced programming: Program specifications, preconditions, postconditions, loop invariants. Linear data structures, searching, and sorting. Algorithm paradigms and analysis.
410 Data Structures (3). Prerequisite, COMP 401. The analysis of data structures and their associated algorithms. Abstract data types, lists, stacks, queues, trees, and graphs. Sorting, searching, hashing.
411 Computer Organization (4). Prerequisite, COMP 401. Digital logic, circuit components. Data representation, computer architecture and implementation, assembly language programming.
416 Introduction to WWW Programming (3). Prerequisite, COMP 401. Developing browser-based applications for the World Wide Web. Introduction to XHTML, CSS, HTTP, and client-side programming. This course is intended for nonmajors and prospective minors and does not count toward the major.
426 Advanced WWW Programming (3). Prerequisite, COMP 410. Developing applications for the World Wide Web including both client-side and server-side programming. Emphasis on Model-View-Controller architecture, AJAX, RESTful Web services, and database interaction.
431 Internet Services and Protocols (3). Prerequisites, COMP 410 and 411. Application-level protocols HTTP, SMTP, FTP, transport protocols TCP and UDP, and the network-level protocol IP. Internet architecture, naming, addressing, routing, and DNS. Sockets programming. Physical-layer technologies. Ethernet, ATM, and wireless.
455 Models of Languages and Computation (3). Prerequisites, COMP 110 or 401, and MATH 381. Introduction to the theory of computation. Finite automata, regular languages, pushdown automata, context-free languages, and Turing machines. Undecidable problems.
486 Applications of Natural Language Processing (INLS 512) (3). See INLS 512 for description.
487 Information Retrieval (INLS 509) (3). See INLS 509 for description.
520 Compilers (3). Prerequisites, COMP 410 and 411. Design and construction of compilers. Theory and pragmatics of lexical, syntactic, and semantic analysis. Interpretation. Code generation for a modern architecture. Run-time environments. Includes a large compiler implementation project.
521 Files and Databases (3). Prerequisites, COMP 410 and 411 and MATH 381. Placement of data on secondary storage. File organization. Database history, practice, major models, system structure and design.
523 Software Engineering Laboratory (4). Prerequisites, COMP 410 and 411. Organization and scheduling of software engineering projects, structured programming, and design. Each team designs, codes, and debugs program components and synthesizes them into a tested, documented program product.
524 Programming Language Concepts (3). Prerequisite, COMP 410. Concepts of high-level programming and their realization in specific languages. Data types, scope, control structures, procedural abstraction, classes, concurrency. Run-time implementation.
530 Operating Systems (3). Prerequisites, COMP 410 and 411. Types of operating systems. Concurrent programming. Management of storage, processes, devices. Scheduling, protection. Case study. Course includes a programming laboratory.
535 Introduction to Computer Security (3). Prerequisites, COMP 410 and MATH 381. Principles of securing the creation, storage, and transmission of data and ensuring its integrity, confidentiality and availability. Topics include access control, cryptography and cryptographic protocols, network security, and online privacy.
536 Enterprise Computing (3). Prerequisite, COMP 426. Designing and building enterprise systems. Basic principles, design considerations, and technologies for large multiserver systems. Requirements include a project in which teams design and build a substantial system.
541 Digital Logic and Computer Design (4). Prerequisite, COMP 411. This course is an introduction to digital logic as well as the structure and electronic design of modern processors. Students will implement a working computer during the laboratory sessions.
550 Algorithms and Analysis (3). Prerequisites, COMP 410 and MATH 381. Formal specification and verification of programs. Techniques of algorithm analysis. Problem-solving paradigms. Survey of selected algorithms.
555 Bioalgorithms (3). Prerequisites, COMP 410 and MATH 381. Bioinformatics algorithms. Topics include DNA restriction mapping, finding regulatory motifs, genome rearrangements, sequence alignments, gene prediction, graph algorithms, DNA sequencing, protein sequencing, combinatorial pattern matching, approximate pattern matching, clustering and evolution, tree construction, Hidden Markov Models, randomized algorithms.
575 Introduction to Computer Graphics (3). Prerequisites, COMP 410 and MATH 547. Hardware, software, and algorithms for computer graphics. Scan conversion, 2-D and 3-D transformations, object hierarchies. Hidden surface removal, clipping, shading, and antialiasing. Not for graduate computer science credit.
580 Enabling Technologies (3). Prerequisite, COMP 410. We will investigate ways computer technology can be used to mitigate the effects of disabilities and the sometimes surprising response of those we intended to help.
585 Serious Games (3). Prerequisite, COMP 410 or 411. Concepts of computer game development and their application beyond entertainment to fields such as education, health, and business. Course includes team development of
590 Topics in Computer Science (1Ė21). Permission of the instructor. This course has variable content and may be taken multiple times for credit.
631 Computer Networks (3). Required preparation, a first course in operating systems, a first course in networking (e.g., COMP 431 and 530), and knowledge of probability and statistics. Topics in computer networks, including link layer protocols, switching, IP, TCP, and congestion control. Additional topics may include peer-to-peer infrastructures, network security, and multimedia applications.
633 Parallel and Distributed Computing (3). Required preparation, a first course in operating systems and a first course in algorithms (e.g., COMP 530 and 550). Principles and practices of parallel and distributed computing. Models of computation. Concurrent programming languages and systems. Architectures. Algorithms and applications. Practicum.
651 Computational Geometry (3). Required preparation, a first course in algorithms (e.g., 550). Design and analysis of algorithms and data structures for geometric problems. Applications in graphics, CAD/CAM, robotics, GIS, and molecular biology.
662 Scientific Computation II (ENVR 662, MATH 662) (3). See MATH 662 for description.
665 Images, Graphics, and Vision (3). Required preparation, a first course in data structures and a first course in discrete mathematics (e.g., COMP 410 and MATH 383). Display devices and procedures. Scan conversion. Matrix algebra supporting viewing transformations in computer graphics. Basic differential geometry. Coordinate systems, Fourier analysis, FDFT algorithm. Human visual system, psychophysics, scale in vision.
Courses for Graduate Students
715 Visualization in the Sciences (MTSC 715, PHYS 715) (3). Computational visualization applied in the natural sciences. For both computer science and natural science students. Available techniques and their characteristics, based on human perception, using software visualization toolkits. Project course.
720 Compilers (3). Prerequisites, COMP 455, 520, and 524. Tools and techniques of compiler construction. Lexical, syntactic, and semantic analysis. Emphasis on code generation and optimization.
721 Database Management Systems (3). Prerequisites, COMP 521 and 550. Database management systems, implementation, and theory. Query languages, query optimization, security, advanced physical storage methods and their analysis.
722 Data Mining (3). Prerequisites, COMP 550 and STOR 435. Data mining is the process of automatic discovery of patterns, changes, associations, and anomalies in massive databases. This course provides a survey of the main topics (including and not limited to classification, regression, clustering, association rules, feature selection, data cleaning, privacy, and security issues) and a wide spectrum of applications.
723 Software Design and Implementation (3). Prerequisites, COMP 524 and 550. Principles and practices of software engineering. Object-oriented and functional approaches. Formal specification, implementation, verification, and testing. Software design patterns. Practicum.
724 Programming Languages (3). Prerequisites, COMP 455, 520, and 524. Selected topics in the design and implementation of modern programming languages. Formal semantics. Type theory. Inheritance. Design of virtual machines. Garbage collection. Principles of restructuring compilers.
730 Operating Systems (3). Prerequisite, COMP 530. Theory, structuring, and design of operating systems. Sequential and cooperating processes. Single processor, multiprocessor, and distributed operating systems.
734 Distributed Systems (3). Prerequisite, COMP 431. Permission of the instructor for students lacking the prerequisite. Design and implementation of distributed computing systems and services. Inter-process communication and protocols, naming and name resolution, security and authentication, scalability, high availability, replication, transactions, group communications, distributed storage systems.
735 Distributed and Concurrent Algorithms (3). Prerequisites, COMP 633 and 723. Verification of concurrent systems. Synchronization; mutual exclusion and related problems, barriers, rendezvous, nonblocking algorithms. Fault tolerance: consensus, Byzantine agreement, self-stabilization. Broadcast algorithms. Termination and deadlock detection. Clock synchronization.
737 Real-Time Systems (3). Prerequisite, COMP 530. Taxonomy and evolution of real-time systems. Timing constraints. Design, implementation, and analysis of real-time systems. Theory of deterministic scheduling and resource allocation. Case studies and project.
740 Computer Architecture and Implementation (3). Prerequisites, COMP 411 and PHYS 352. Architecture and implementation of modern single-processor computer systems. Performance measurement. Instruction set design. Pipelining. Instruction-level parallelism. Memory hierarchy. I/O system. Floating-point arithmetic. Case studies. Practicum.
741 Elements of Hardware Systems (3). Prerequisite, COMP 411. Issues and practice of information processing hardware systems for computer scientists with little or no previous hardware background. System thinking, evaluating technology alternatives, basics of electronics, signals, sensors, noise, and measurements.
744 VLSI Systems Design (3). Prerequisite, COMP 740. Required preparation, knowledge of digital logic techniques. Introduction to the design, implementation, and realization of very large-scale integrated systems. Each student designs a complete digital circuit that will be fabricated and returned for testing and use.
750 Algorithm Analysis (3). Prerequisites, COMP 455 and 550. Algorithm complexity. Lower bounds. The classes P, NP, PSPACE and co-NP; hard and complete problems. Pseudo-polynomial time algorithms. Advanced data structures. Graph-theoretic, number-theoretic, probabilistic, and approximation algorithms.
752 Mechanized Mathematical Inference (3). Prerequisite, COMP 825. Propositional calculus. Semantic tableaux. Davis-Putnam algorithm. Natural deduction. First-order logic. Completeness. Resolution. Problem representation. Abstraction. Equational systems and term rewriting. Specialized decision procedures. Nonresolution methods.
761 Introductory Computer Graphics (1). A computer graphics module course with one credit hour of specific COMP 665 content.
762 Discrete Event Simulation (STOR 762) (3). See STOR 762 for description.
763 Semantics and Program Correctness (3). Prerequisite, COMP 724. Formal characterization of programs. Denotational semantics and fixed-point theories. Proof of program correctness and termination. Algebraic theories of abstract data types. Selected topics in the formalization of concurrent computation
764 Monte Carlo Method (3). Prerequisites, COMP 110, MATH 233, 416, 418, and STAT 435. Permission of the instructor for students lacking the prerequisites. Relevant probability and statistics. General history. Variance reduction for sums and integrals. Solving linear and nonlinear equations. Random, pseudorandom generators; random trees. Sequential methods. Applications.
766 Visual Solid Shape (3). Prerequisites, MATH 233 and 416. 3D differential geometry; local and global shape properties; visual aspects of surface shape. Taught largely through models and figures. Applicable to graphics, computer vision, human vision, and biology.
767 Geometric and Solid Modeling (3). Prerequisites, COMP 575 or 770, and MATH 661. Curve and surface representations. Solid models. Constructive solid geometry and boundary representations. Robust and error-free geometric computations. Modeling with algebraic constraints. Applications to graphics, vision, and robotics.
768 Physically Based Modeling and Simulation (3). Prerequisite, COMP 665.Permission of the instructor for students lacking the prerequisite. Geometric algorithms, computational methods, simulation techniques for modeling based on mechanics and its applications.
770 Computer Graphics (3). Prerequisites, COMP 665 and 761. Study of graphics hardware, software and applications. Data structures, graphics, languages, curve surface and solid representations, mapping, ray tracing and radiosity.
775 Image Processing and Analysis (BMME 775) (3). See BMME 775 for description.
776 Computer Vision in our 3D World (3). Prerequisites, MATH 416, 566, COMP 550, 665, and 775. Permission of the instructor for students lacking the prerequisites. Fundamental problems of computer vision. Projective geometry. Camera models, camera calibration. Shape from stereo, epipolar geometry. Photometric stereo. Optical flow, tracking, motion. Range finders, structured light. Object recognition.
787 Visual Perception (3). Prerequisites, COMP 665 and PSYC 730. Surveys form, motion, depth, scale, color, brightness, texture and shape perception. Includes computational modeling of vision, experimental methods in visual psychophysics and neurobiology, recent research and open questions.
788 Expert Systems (3). Prerequisite, COMP 750. Languages for knowledge engineering. Rules, semantic nets, and frames. Knowledge acquisition. Default logics. Uncertainties. Neural networks.
790 Topics in Computer Science (1Ė21). Permission of the instructor. This course has variable content and may be taken multiple times for credit.
822 Topics in Discrete Optimization (STOR 822) (3). See STOR 822 for description.
824 Functional Programming (3). Prerequisite, COMP 524. Programming with functional or applicative languages. Lambda calculus; combinators; higher-order functions; infinite objects. Least fixed points, semantics, evaluation orders. Sequential and parallel execution models.
825 Logic Programming (3). Prerequisite, COMP 524. Propositional calculus, Horn clauses, first-order logic, resolution. Prolog: operational semantics, relationship to resolution, denotational semantics, and nonlogical features. Programming and applications. Selected advanced topics.
831 Internet Architecture and Performance (3). Prerequisite, COMP 431. Permission of the instructor for students lacking the prerequisite. Internet structure and architecture; traffic characterization and analysis; errors and error recovery; congestion and congestion control; services and their implementations; unicast and multicast routing.
832 Multimedia Networking (3). Prerequisites, COMP 431 and 530. Audio/video coding and compression techniques and standards. Media streaming and adaptation. Multicast routing, congestion, and error control. Internet protocols RSVP, RTP/RTCP. Integrated and differentiated services architecture for the Internet.
841 Advanced Computer Architecture (3). Prerequisite, COMP 740. Concepts and evolution of computer architecture, machine language syntax and semantics; data representation; naming and addressing; arithmetic; control structures; concurrency; input-output systems and devices. Milestone architectures.
842 Advanced Computer Implementation (3). Prerequisite, COMP 740. Required preparation, knowledge of digital logic techniques. The application of digital logic to the design of computer hardware. Storage and switching technologies. Mechanisms for addressing, arithmetic, logic, input/output and storage. Microprogrammed and hardwired control.
844 Advanced Design of VLSI Systems (3). Prerequisite, COMP 744. Advanced topics in the design of digital MOS systems. Students design, implement, and test a large custom integrated circuit. Projects emphasize the use of advanced computer-aided design tools.
850 Advanced Analysis of Algorithms (3). Prerequisite, COMP 750. Design and analysis of computer algorithms. Time and space complexity; absolute and asymptotic optimality. Algorithms for searching, sorting, sets, graphs, and pattern-matching. NP-complete problems and provably intractable problems.
870 Advanced Image Synthesis (3). Prerequisite, COMP 770. Advanced topics in rendering, including global illumination, surface models, shadings, graphics hardware, image-based rendering, and antialiasing techniques. Topics from the current research literature.
872 Exploring Virtual Worlds (3). Prerequisite, COMP 870. Project course, lecture, and seminar on real-time interactive 3D graphics systems in which the user is "immersed" in and interacts with a simulated 3D environment. Hardware, modeling, applications, multi-user systems.
875 Recent Advances in Image Analysis (3). Prerequisite, COMP 775. Lecture and seminar on recent advances in image segmentation, registration, pattern recognition, display, restoration, and enhancement.
892 Practicum (0.5). Permission of the instructor. Work experience in an area of computer science relevant to the studentís research interests and pre-approved by the instructor. The grade, pass or fail only, will depend on a written report by the student and on a written evaluation by the employer.
910 Computer Science Module (0.5Ė21). A variable-credit module course that can be used to configure a registration for a portion of a class.
911 Professional Writing in Computer Science (3). Graduate computer science majors only. Analysis of good and bad writing. Exercises in organization and composition. Each student also writes a thesis-quality short technical report on a previously approved project.
915 Technical Communication in Computer Science (1). Graduate computer science majors or permission of the instructor. Seminar on teaching, short oral presentations, and writing in computer science.
916 Seminar in Professional Practice (1). Required preparation, satisfaction of M.S. computer science program product requirement. The role and responsibilities of the computer scientist in a corporate environment, as an entrepreneur, and as a consultant. Professional ethics.
917 Seminar in Research (1). Graduate computer science majors only. The purposes, strategies, and techniques for conducting research in computer science and related disciplines.
918 Research Administration for Scientists (3). Graduate standing required. Introduction to grantsmanship, research grants and contracts, intellectual property, technology transfer, conflict of interest policies. Course project: grant application in NSF FastLane.
980 Computers and Society (1). Graduate computer science majors only. Seminar on social and economic effects of computers on such matters as privacy, employment, power shifts, rigidity, dehumanization, dependence, quality of life.
990 Research Seminar in Computer Science (1Ė21). Permission of the instructor. Seminars in various topics offered by members of the faculty.
991 Reading and Research (1Ė21). Permission of the instructor. Directed reading and research in selected advanced topics.
992 Computer Science Masterís Project (Non Thesis Option) (3Ė21). Permission of the department.
993 Masterís Thesis (3Ė6). Permission of the department.
994 Doctoral Dissertation (3Ė9). Permission of the department.