Focusing on a number of advanced courses with close research collaboration with our faculty, this program will well equip our graduate students both for working in industry and also for continuing in a university-based research career.
Prospective graduate students are encouraged to contact a faculty member in their area of research interest before submitting an application.
- Application and Acceptance
- English Language Comptency
- Program Completion Process
- Financial Support
- Faculty Research Interests
- A Departmental Graduate Committee is to be composed of three Departmental faculty members, with a Chair elected by the Department faculty for a 3-year term. The other two members are to be elected for two year terms (initial appointments of one and two year terms). It is the function of the Graduate Committee to receive applications, seek appropriate supervisors for applicants, ensure that the applicants meet the requirements of acceptance, and make recommendations to the Graduate Studies Committee of the University.
- Incoming Graduate students should have a Bachelor’s degree or equivalent in Computer Science with a 70% average or better; equivalent to grade B or better, or 3.3 or better on the Merit Point system (out of 4). A degree in a related field is acceptable if the applicant shows evidence of a Computer Science background suitable for entry into the Master’s program as judged by the Departmental Graduate Committee. Generally, the minimum background is the equivalent of all required courses of a St.F.X. Honours Computer Science degree. Some applicants may be requested to take make-up courses to satisfy the minimal requirements.
- Prospective graduate students are encouraged to submit scores of Graduate Record Examinations or equivalent.
- Undergraduate transcripts and two letters of recommendation are required. Forms are available online at: http://www.mystfx.ca/academic/graduate-studies/index.html.
The application form can be found online at: https://sites.stfx.ca/graduate_studies/node/38.html
- All students accepted into the program must have a supervisor, who must be a regular faculty member in the Department of Computer Science at St. Francis Xavier University. See the Faculty listing for areas of specialization.
- Final acceptance is the decision of the University Graduate Studies Committee.
English Language Competency
As the standard language of study at St. Francis Xavier University is English, candidates whose native language is not English must demonstrate their competency in the English language. The standard test is the TOEFL (Test of English as a Foreign Language). A minimum acceptable score of 580 for the written TOEFL or 236 for the computer-based version is required. The following other tests will be accepted with the specified minimum scores: IBT (internet based TOEFL) – 92; MELAB - 90; IELTS – 6.5; CanTest – average of at least 4.5 with no band score lower than 4.0; CAEL – 60 overall, with no band score lower than 50. The TOEFL is waived if the applicant has completed a degree at an institution where the language of instruction is English. Further information may be obtained at:
TOEFL – www.toefl.org
MELAB – www.melab.ca
IELTS – www.ielts.org
CanTest – http://www.cantest.uottawa.ca/
CAEL – www.cael.ca
Applications can be found at the following site:
Limited financial Support (scholarship or graduate research assistantships) is available. St.Francis Xavier University currently has a limited number of positions available for students enrolled in the MSc program to work as laboratory instructors. In addition, prospective graduate students are strongly urged to apply for Science and Engineering Research Canada Post Graduate Scholarships (Canadian citizens or landed immigrants) or for Commonwealth Scholarships tenable at St. Francis Xavier University (citizens of Commonwealth countries).
Content-Based Image Retrieval
The development of machine learning algorithms for real world applications. Although I am interested in many algorithms, particular strategies of interest are Evolutionary Computation and Artificial Neural Networks. Application areas include neuroinformatics, bioinformatics, kinematics, geology, art, finance, and clinical applications.
Using computational technology and imaging to investigate neurodevelopmental disorders and healthy brain development
Real-time and embedded systems
Model checking/Verification, Model Driven Engineering, Ontology Reasoning, Applications to Healthcare
Combinatorial Optimization in relation to approximation algorithms
Laurence T. Yang
Parallel and distributed computing, embedded and ubiquitous computing