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School of Computing Science | Faculty of Applied Sciences Simon Fraser University Calendar | Spring 2022

Computing Science Dual Degree

Doctor of Philosophy

Students in this graduate dual degree program (GDDP), jointly developed by Simon Fraser University and Zhejiang University (ZJU), China, will acquire two graduate degrees. Graduates will receive a doctor of philosophy (PhD) degree from Simon Fraser University, and a second doctor of philosophy (PhD) degree from Zhejiang University. Students will study and conduct research at both universities.

The language of instruction at Simon Fraser University is English, while at Zhejiang University, it is either English and/or Chinese.

Admission Requirements

Students must be admitted to one university, and then apply and be admitted to the other university.

To qualify for admission, students must satisfy the usual admission requirements as specified by each university. At SFU, the admission requirements are stated in Graduate General Regulations 1.3 in the SFU Calendar. The university the student is first admitted to will be referred to as the student's 'home' university.

Once admitted to the home university, the student may then apply for admission to the graduate dual degree program, normally within 18 months of admission to their home university graduate program. The program application requires the support and involvement of the student's supervisor at the home university. The graduate program committee at the home university decides whether to recommend the student for admission to the GDDP. A recommended individual's application will then be forwarded to the other 'partner' university. Applicants must meet the admission requirements of that partner university.

Program Requirements

SFU students with an MSc in computing science or an equivalent must complete at least six units at each university from the courses listed in groups I, II, and III. Please see requirements for the students without the MSc in computing science or an equivalent in Other Information.

This program consists of course work, depth requirements, and a thesis.

All students complete one of

CMPT 705 - Design and Analysis of Algorithms (3)

The objective of this course is to expose students to basic techniques in algorithm design and analysis. Topics will include greedy algorithms, dynamic programming, advanced data structures, network flows, randomized algorithms. Students with credit for CMPT 706 may not take this course for further credit.

Section Instructor Day/Time Location
G100 Igor Shinkar
Mo 12:30 PM – 2:20 PM
We 12:30 PM – 1:20 PM
AQ 3159, Burnaby
AQ 3159, Burnaby
CMPT 710 - Computational Complexity (3)

This course provides a broad view of theoretical computing science with an emphasis on complexity theory. Topics will include a review of formal models of computation, language classes, and basic complexity theory; design and analysis of efficient algorithms; survey of structural complexity including complexity hierarchies, NP-completeness, and oracles; approximation techniques for discrete problems. Equivalent Courses: CMPT810.

2122001-2 Elements of the Theory of Computation (ZJU course)

and one course from Group II

and one course from Group III

and a thesis

CMPT 899 - PhD Thesis (6)

Graded on a satisfactory/unsatisfactory basis.

Section Instructor Day/Time Location
G100 TBD

NOTE: A Simon Fraser University course and a Zhejiang University course are deemed similar if the two courses overlap substantially. Students with credit for one of two similar courses may not complete the other course for further credit. Simon Fraser University's graduate program breadth committee and the corresponding Zhejiang University committee will decide on the list of similar courses.

Courses

Group I: Algorithms and Theory Credits

Courses at Simon Fraser University

CMPT 701 - Computability and Logic (3)

Deep connections between logic and computation have been evident since early work in both areas. More recently, logic-based methods have led to important progress in diverse areas of computing science. This course will provide a foundation in logic and computability suitable for students who wish to understand the application of logic in various areas of CS, or as preparation for more advanced study in logic or theoretical CS.

CMPT 705 - Design and Analysis of Algorithms (3)

The objective of this course is to expose students to basic techniques in algorithm design and analysis. Topics will include greedy algorithms, dynamic programming, advanced data structures, network flows, randomized algorithms. Students with credit for CMPT 706 may not take this course for further credit.

Section Instructor Day/Time Location
G100 Igor Shinkar
Mo 12:30 PM – 2:20 PM
We 12:30 PM – 1:20 PM
AQ 3159, Burnaby
AQ 3159, Burnaby
CMPT 710 - Computational Complexity (3)

This course provides a broad view of theoretical computing science with an emphasis on complexity theory. Topics will include a review of formal models of computation, language classes, and basic complexity theory; design and analysis of efficient algorithms; survey of structural complexity including complexity hierarchies, NP-completeness, and oracles; approximation techniques for discrete problems. Equivalent Courses: CMPT810.

CMPT 711 - Bioinformatics Algorithms (3)

Fundamental algorithmic techniques used to solve computational problems encountered in molecular biology. This area is usually referred to as Bioinformatics or Computational Biology. Students who have taken CMPT 881 (Bioinformatics) in 2007 or earlier may not take CMPT 711 for further credit.

CMPT 813 - Computational Geometry (3)

This course covers recent developments in discrete, combinatorial, and algorithmic geometry. Emphasis is placed on both developing general geometric techniques and solving specific problems. Open problems and applications will be discussed.

CMPT 815 - Algorithms of Optimization (3)

This course will cover a variety of optimization models, that naturally arise in the area of management science and operations research, which can be formulated as mathematical programming problems. Equivalent Courses: CMPT860.

CMPT 981 - Special Topics in Theoretical Computing Science (3)

Section Instructor Day/Time Location
G100 Andrei Bulatov
Tu 2:30 PM – 4:20 PM
Th 2:30 PM – 3:20 PM
AQ 5019, Burnaby
AQ 5046, Burnaby
G101 Th 3:30 PM – 4:20 PM
AQ 5046, Burnaby

Courses at Zhejiang University

2111001-2 Applied Mathematics for Computer Science (1)

2111002-2 Applied Mathematics for Computer Science (2)

2122001-2 Elements of the Theory of Computation

2122019-2 Advanced Formal Methods

Group II: Systems

Courses at Simon Fraser University

CMPT 770 - Parallel and Distributed Computing (3)

Principles involved in designing modern parallel and distributed software systems. The course focuses on covering key concepts like concurrency, synchronization, consistency models and fault tolerance. Involves multiple programming projects and reading articles on recent trends in parallel and distributed computing.

CMPT 771 - Computer Networks (3)

Investigates the design and operation of wide-area computer networks, especially the Internet and the TCP/IP protocol suite. This course studies performance modeling, security and quality of service; wireless connectivity and multimedia networking; network services, including recent topics and trends in these areas.

CMPT 777 - Formal Verification (3)

The goal of formal verification is to prove correctness or to find mistakes in software and other systems. This course introduces, at an accessible level, a formal framework for symbolic model checking, one of the most important verification methods. The techniques are illustrated with examples of verification of reactive systems and communication protocols. Students learn to work with a model checking tool such as NuSMV.

CMPT 982 - Special Topics in Networks and Systems (3)

Section Instructor Day/Time Location
G100 Tu 8:30 AM – 10:20 AM
Fr 8:30 AM – 9:20 AM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby

Courses at Zhejiang University

2122002-2 Advanced Operating System

2122003-2 Advanced Computer Architecture

2122006-2 Modern VLSI Design: System on Chip Design

2122018-2 Advanced Computer Networks

2124003-2 Computer Security

2124012-2 Grid Computing and Distributed Systems

2124014-2 Advanced Software Engineering

2124028-2 Pervasive Computing

2124057-2 High End Computing and Its Applications

2124058-2 Advanced Topics in Compilers

2124059-2 Multi-core Computing

2124060-2 Network Multimedia Computing

2124072-2 Principles of Embedded System Design

2124069-2 Sensor Network and Information Processing

2124070-2 Parallel Computer Architecture and Programming

2124071-2 Network Algorithms

Group III: Applications

Courses at Simon Fraser University

CMPT 713 - Natural Language Processing (3)

Natural Language Processing is the automatic analysis of human languages such as English, Korean, and thousands of others analyzed by computer algorithms. Unlike artificially created programming languages where the structure and meaning of programs is easy to encode, human languages provide an interesting challenge, both in terms of its analysis and the learning of language from observations. Covers NLP tasks such as language modeling, machine translation, multilingual processing, information extraction, question answering, and other topics relevant to modern NLP. Students with credit for CMPT 825 or CMPT 413 may not take this course for further credit.

CMPT 720 - Robotic Autonomy: Algorithms and Computation (3)

Fundamental concepts in robotics and related fields, including computational methods for solving decision making and algorithms for robots to understand their environment. Topics include modeling and simulation of robotic systems, optimization, optimal control, robotic safety, reinforcement learning, and robotic perception. Applications of the material include unmanned aerial vehicles and self-driving cars.

CMPT 721 - Knowledge Representation and Reasoning (3)

Knowledge representation is the area of Artificial Intelligence concerned with how knowledge can be represented symbolically and manipulated by reasoning programs. This course addresses problems dealing with the design of languages for representing knowledge, the formal interpretation of these languages and the design of computational mechanisms for making inferences. Since much of Artificial Intelligence requires the specification of a large body of domain-specific knowledge, this area lies at the core of AI. Prerequisite: CMPT 310/710 recommended. Cross-listed course with CMPT 411.

CMPT 726 - Machine Learning (3)

Machine Learning is the study of computer algorithms that improve automatically through experience. Provides students who conduct research in machine learning, or use it in their research, with a grounding in both the theoretical justification for, and practical application of, machine learning algorithms. Covers techniques in supervised and unsupervised learning, the graphical model formalism, and algorithms for combining models. Students who have taken CMPT 882 (Machine Learning) in 2007 or earlier may not take CMPT 726 for further credit.

Section Instructor Day/Time Location
G200 Ke Li
We 5:30 PM – 7:20 PM
Fr 5:30 PM – 6:20 PM
SWH 10081, Burnaby
SWH 10081, Burnaby
CMPT 727 - Statistical Machine Learning (3)

Statistical foundation for machine learning algorithms, emphasizing bias-variance tradeoff. Students will learn principles for choosing effective methods and tailoring them to fit a given learning problem. Potential topics include probabilistic graphical models, maximum likelihood estimation, latent variables and the EM algorithm, convex optimization, and variational and sampling-based methods.

Section Instructor Day/Time Location
G100 Maxwell Libbrecht
Mo 10:30 AM – 11:20 AM
Th 10:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
CMPT 740 - Database Systems (3)

Introduction to advanced database system concepts, including query processing, transaction processing, distributed and heterogeneous databases, object-oriented and object-relational databases, data mining and data warehousing, spatial and multimedia systems and Internet information systems.

CMPT 741 - Data Mining (3)

The student will learn basic concepts and techniques of data mining. Unlike data management required in traditional database applications, data analysis aims to extract useful patterns, trends and knowledge from raw data for decision support. Such information are implicit in the data and must be mined to be useful.

Section Instructor Day/Time Location
G100 Ke Wang
We 1:30 PM – 2:20 PM
Fr 12:30 PM – 2:20 PM
AQ 3005, Burnaby
AQ 3005, Burnaby
CMPT 762 - Computer Vision (3)

Selected topics in computer vision including cameras, edge detection, feature matching, optical flow, alignment, epipolar geometry, stereo, structure-from-motion, recognition, segmentation, detection, and deep learning.

Section Instructor Day/Time Location
G100 Yasutaka Furukawa
We 3:30 PM – 4:20 PM
Fr 2:30 PM – 4:20 PM
AQ 3153, Burnaby
AQ 3153, Burnaby
CMPT 763 - Biomedical Computer Vision (3)

Selected topics in biomedical imaging. Computer visions, medical data and image representation, file formats, segmentation, registration, classification, anatomical shape modeling, machine and deep learning tools and methods.

CMPT 764 - Geometric Modelling in Computer Graphics (3)

Advanced topics in geometric modelling and processing for computer graphics, such as Bezier and B-spline techniques, subdivision curves and surfaces, solid modelling, implicit representation, surface reconstruction, multi-resolution modelling, digital geometry processing (e.g., mesh smoothing, compression, and parameterization), point-based representation, and procedural modelling. Prerequisite: CMPT 361, MACM 316. Students with credit for CMPT 464 or equivalent may not take this course for further credit.

CMPT 766 - Computer Animation and Simulation (3)

Selected topics in computer animation and simulation, including 3D character animation and control, facial animation, simulation of natural phenomena (i.e. fluids, crowd simulation, and deformation of pliant materials).

Section Instructor Day/Time Location
G100 KangKang Yin
Mo 10:30 AM – 11:20 AM
We 10:30 AM – 11:20 AM
Fr 10:30 AM – 11:20 AM
AQ 3159, Burnaby
AQ 3159, Burnaby
AQ 3153, Burnaby
CMPT 767 - Visualization (3)

Advanced topics in data visualization. Topics covered may include principles of data representation, data presentation, data interaction, data physicalization, data and visualization literacy, data visualization and diversity, open data, and public personal data visualization. Students with credit for CMPT 878 or 775 may not take this course for further credit.

CMPT 820 - Multimedia Systems (3)

This seminar course covers current research in the field of multimedia computing. Topics include multimedia data representation, compression, retrieval, network communications and multimedia systems. Computing science graduate student or permission of instructor. Equivalent Courses: CMPT880.

CMPT 822 - Computational Vision (3)

A seminar based on the artificial intelligence approach to vision. Computational vision has the goal of discovering the algorithms and heuristics which allow a two dimensional array of light intensities to be interpreted as a three dimensional scene. By reading and discussing research papers - starting with the original work on the analysis of line drawings, and ending with the most recent work in the field - participants begin to develop a general overview of computational vision, and an understanding of the current research problems.

CMPT 827 - Intelligent Systems (3)

Intelligent systems are knowledge-based computer programs which emulate the reasoning abilities of human experts. This introductory course will analyze the underlying artificial intelligence methodology and survey advances in rule-based systems, constraint solving, incremental reasoning, intelligent backtracking and heuristic local search methods. We will look specifically at research applications in intelligent scheduling, configuration and planning. The course is intended for graduate students with a reasonable background in symbolic programming.

CMPT 829 - Special Topics in Bioinformatics (3)

Examination of recent literature and problems in bioinformatics. Within the CIHR graduate bioinformatics training program, this course will be offered alternatively as the problem-based learning course and the advanced graduate seminar in bioinformatics (both concurrent with MBB 829). Prerequisite: Permission of the instructor.

CMPT 843 - Database and Knowledge-base Systems (3)

An advanced course on database systems which focuses on data mining and data warehousing, including their principles, designs, implementations, and applications. It may cover some additional topics on advanced database system concepts, including deductive and object-oriented database systems, spatial and multimedia databases, and database-oriented Web technology.

CMPT 983 - Special Topics in Artificial Intelligence (3)

Section Instructor Day/Time Location
G100 Angel Chang
Mo 10:30 AM – 11:50 AM
We 10:30 AM – 11:50 AM
AQ 5016, Burnaby
BLU 10921, Burnaby
CMPT 985 - Special Topics in Graphics, HCI, Visualization, Vision, Multimedia (3)

Examines current research topics in computer graphics, human computer interaction (including audio), computer vision and visualization.

Section Instructor Day/Time Location
G200 Tu 10:30 AM – 12:20 PM
Fr 10:30 AM – 11:20 AM
AQ 5016, Burnaby
AQ 4150, Burnaby

Courses at Zhejiang University

0711026-2 Bioinformatics Topics

2112001-2 Research Frontiers of Computer Science and Technology

2122020-4 Computer Graphics

2122021-2 Introduction to Computer Vision

2122023-2 Introduction to Artificial Intelligence

2124017-2 The Fundamental Principles of Non-Photorealistic Computer Graphics

2124025-2 Electronic Business Technology

2124027-2 Computer Animation and Its Application

2124061-2 Network Multimedia Search Engine

2124062-2 Solid Modeling

2124063-2 Biologic Intelligence and Algorithm

2124064-2 Introduction to Machine Learning

2124065-2 Advanced Artificial Intelligence

2124066-2 Visualization in Scientific Computing

2124067-2 Speech and Language, Processing and Understanding

2124068-2 Image Processing and Modeling

2124073-2 Virtual Reality

2124074-2 HCI and Virtual Human

2124075-2 Data Mining

5141062-2 Advanced Data Base Technology

Program Length

Students entering with a master of science degree (MSc) in computing science, or equivalent, are expected to complete the program requirements in four years. Students entering with a bachelor of science degree (BSc) are expected to complete the program requirements in six years.

Other Information

Course Work for Simon Fraser University Students

SFU students without an MSc in computing science, or an equivalent, must complete at least 12 units at each university. Of those units, at least nine from each university must be chosen from the courses listed in groups I, II, and III. At Zhejiang University, the approval of the student's supervisor and the Zhejiang University graduate program director are required for the student to complete courses that are not listed in groups I, II, and III.

SFU students with an MSc in computing science or an equivalent must complete at least six units at each university from the courses listed in groups I, II, and III.

With the approval of the student's supervisors and the graduate program director at the partner (home) university, up to three of the units at the partner (home) university may be completed at the home (partner) university.

Course Work for Zhejiang University Students

ZJU students without an MSc in computing science or an equivalent must complete at least 12 units at Simon Fraser University and at least 14 units at Zhejiang University. Of those units, all but three from Simon Fraser University must be chosen from the courses listed in groups I, II, and III.

ZJU students with an MSc in computing science or an equivalent must complete at least six units at each university from the courses listed in groups I, II, and III.

Depth Requirement

Simon Fraser University's School of Computing Science requires students to complete all of the following depth requirements:

  • present a depth seminar and examination
  • write a thesis proposal and present a seminar and defend the material
  • submit a written thesis proposal and defend that thesis based on independent original work

All requirements may be completed at either university. Additional requirements concerning the thesis are found at the graduate and postdoctoral studies website. For more information about the thesis defence, see 1.9 and 1.10 of Simon Fraser University's graduate general regulations.

Breadth Requirement

At most two special topics courses at Simon Fraser University may be used to meet the breadth requirement, except with permission from the graduate program director at Simon Fraser University. For more information about breadth requirements, please visit the GDDP website.

Supervisory Committee

Each student will be supervised by a supervisory committee consisting of a supervisor and another faculty member at the home university, and a co-supervisor and another faculty member at the partner university.

Each student is required to have an annual progress evaluation by the supervisory committee. Meetings of the supervisory committee are normally once per year and may involve the use of new media.

Program Withdrawal

Student may withdraw from the GDDP program by transferring to the PhD program at the home or partner university at any time. The full academic record at the partner university may be used to determine standing at the home university.

Residency Requirement

Students are expected to conduct research at both Simon Fraser University and Zhejiang University, and to reside at each university for at least one year.

Tuition Fees

Students who are resident at Simon Fraser University pay per term tuition fees. Students who are resident at Zhejiang University pay per year tuition fees.

Academic Requirements within the Graduate General Regulations

All graduate students must satisfy the academic requirements that are specified in the Graduate General Regulations, as well as the specific requirements for the program in which they are enrolled.