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Master of Visual Computing
Overview
Diagnosing diseases from medical imaging, analyzing complex satellite imaging and designing navigation systems for autonomous vehicles are skills that are in high demand. With SFU’s Master of Visual Computing program, students can master the applied skills through real-world training at the companies that drive AI, XR, and other visual tech.
Designed with a collaborative cohort model and taught by a blend of academic experts and seasoned industry professionals, this program equips students with both the theoretical knowledge and practical skills needed to thrive in the tech sector. Whether you are a recent graduate, an experienced professional looking to transition into technology, or someone aiming to advance in your current role, the program provides a strong foundation to help you reach your career goals.
Applications are open from now until January 19, 2026.
Program Highlights
- 16 - 20 Months Completion
- Industry-Aligned Curriculum
- Gain industry-relevant experience with our paid co-op placement
- Hands-on technical training
- Dedicated career support to help you land your next role
1st
and only visual computing program of its kind in Canada
#1
in Canada for computer vision publications
CSrankings.org (2012-2022)
#1
IN CANADA FOR computer graphics publications
#10 in the world
CSrankings.org (2012-2022)
Program Highlights
Unlike traditional thesis-based degrees, this program does not have a research component. Instead, almost half of the coursework consists of hands-on lab training, complemented by a carefully selected array of instructional courses. Students develop deep knowledge and practical skills working with data in all forms. Consulting with dedicated academic advisors, students are able to select courses that help them hone in on an area of interest.
A hallmark of our program is the mandatory, paid co-op placement. Co-op allows students to tackle real-world scientific, engineering and socio-economic problems while gaining valuable project management experience and expanding their network of industry contacts.
Curriculum
The current curriculum of the Master of Visual Computing covers (but is not limited to) the following topics:
- Fundamentals and advanced knowledge related to computer vision, computer graphics, and image processing.
- Machine learning, deep learning, and data-driven techniques in visual computing.
- Pattern and action recognition in images and video.
- Visual data acquisition, e.g. computational photography, laser scanning, and geometry and motion tracking.
- Visual computing on specialized data and applications, e.g. medical, simulation, AR/VR, 3D games, and robotics, etc.
- Computational design and fabrication, e.g. 3D printing.
Admission Requirements
Admission to this master's programs is competitive: only the best qualified applicants are offered a seat. Therefore, it is imperative that students familiarize themselves with the admission requirements in order to ensure they submit a strong application. Since conditional and qualifying admission offers are made very rarely and only in exceptional cases, students who qualify for regular admission have higher chances of acceptance than those who only qualify for conditional or qualifying admission.
Foundations:
In order for students to succeed in this program, it is expected that they have the following knowledge/experience prior to beginning their studies:
- Ability to program in Java, Python and C++. Familiarity with programming and ability to learn new languages. Students planning to take courses in computer graphics, computer vision, etc. may not need Java and C++ but need Matlab.
- Knowledge of advanced math - calculus, linear algebra and advanced statistics.
- Knowledge of data structures and algorithms, databases, and operating systems.
Regular Admission:
Regular admission is the preferred route for this program. To qualify, students must satisfy the admission requirements laid out here as well as the University's admission requirements for a master's program, as stated in SFU Graduate Regulation 1.3.6a.
STUDENTS WHO HAVE COMPLETED THEIR DEGREES IN CANADA
Students must hold a bachelor's degree or equivalent in computer science or a related field with a cumulative grade point average (CGPA) of at least 3.00/4.33 (B) or the equivalent. Alternatively, a minimum GPA of 3.33/4.33 on the last 60 credits of undergraduate courses will also meet the GPA requirements for admission to the program. All graduate work is also considered.
Students who have completed their degrees outside of Canada
Students applying to this program must hold a bachelor's degree or equivalent in computer science or a related field. Please see here for minimum equivalent qualifications, academic standing and required credential for students who have completed their degree outside of Canada. The academic requirement is listed by country.
CONDITIONAL ADMISSION:
In exceptional circumstances, a student may be admitted with lower formal qualifications when there is significant professional experience relevant to the proposed area of scholarship. Please use your application materials to make the case that your professional experience is relevant and should be considered along with your GPA.
For more information on conditional admission, please review Graduate Admission Regulations 1.3.8.
Please note: Conditional admissions are offered only in exceptional circumstances.
ENGLISH LANGUAGE COMPETENCY
The language of instruction, examination and communication in our program is English. Students whose primary language is not English must meet SFU's English proficiency requirements as set out in the Graduate General Regulation 1.3.3. Applicants who have completed a degree at a recognized post-secondary institution where the language of instruction and examination is English in a country where English is the primary language are not required to submit proof of English proficiency. Please view the list of accepted countries here.
All other applicants are required to provide proof of English proficiency. For more detailed information on the requirements, please visit the Graduate Studies page on English Language Requirements.
Meet Our Students
Reza Asad
Master of Science in Visual Computing, Class of 2020
Previous Education:
Bachelor of Science (Hons) in Applied Mathematics, University of Toronto
Previous Experience:
Data Scientist at Tamr, San Francisco Bay Area
While I was working I spent a lot of time outside my job to learn about cutting-edge developments in machine learning. In particular, a machine learning conference at Google led by Ian Goodfellow and his research group sparked my curiosity about deep learning and its applications to computer vision. The conference as well as the readings I completed afterwards were the main factors that inspired me to pursue a graduate degree where I could focus on visual data.
Jayleen Zhou
Master of Visual Computing, Class of 2020
Previous Education:
Bachelor of Science in Computer Science & Bachelor of Arts in Economics with a minor in Philosophy, Neuroscience, and Psychology, Washington University, St. Louis
Previous Experience:
Advertising Creative Developer at Fancy Pants Group, New York City
I was preparing a pitch for a client when I came across a video demo of an augmented reality installation that Purina had created in a shopping mall. The installation allowed customers to interact with Purina's cartoon mascot ‘Felix the cat’. Watching the video, I suddenly understood the appeal of augmented reality. I believe adding more of these kinds of installations in our daily lives will change the way we live, and I want to be one of the people who can create such content.
Other program options
- Big Data (Master of Science)
- Computing Science (Master of Science)
- Computing Science Dual Degree (Master of Science)
- Cybersecurity (Master of Cybersecurity)
- Visual Computing (Master of Visual Computing)
- Computing Science (Doctor of Philosophy)
- Computing Science Dual Degree (Doctor of Philosophy)
- Big Data (Graduate Diploma)
- Cybersecurity (Graduate Diploma)
- Visual Computing (Graduate Diploma)