New frontiers in computing
In 2018, the Lassonde School of Engineering at York University introduced a Specialization in Artificial Intelligence (AI) in its Master of Science of Computer Science program. While AI-based research is still pursued in the general stream of the program, students in this specialization take six graduate courses, of which at least five are within the area of AI, in their first two terms. In addition, students conduct a research project that applies AI to a practical problem under the supervision of faculty members and in collaboration with partners in the private or public sector. With this knowledge, our graduates will be positioned to successfully deploy AI methodologies across many sectors.
Our Specialization in AI is officially recognized by the Vector Institute, giving our students access to the Vector Scholarships in Artificial Intelligence.
Note that the AI specialization is meant as a targeted preparation to apply AI concepts in the workplace, but being non-thesis it is not suitable to further pursue doctoral studies. Those interested in AI-based research in a thesis program should apply to the MSc in Computer Science program.
It will be the student’s responsibility to secure an internship for their research project. The Vector Institute facilitates the process by providing students in this program opportunities to interact with potential employers through networking events and the Vector Digital Talent Hub.
Below you will find a list of faculty members who are part of the Graduate Program in Electrical Engineering and Computer Science. It mentions their areas of research interests within AI (many have other research interests as well) and provides a link to their personal homepage or research group for more information about their research.
• An honours degree in Computer Science or equivalent, with at least a B+ average in the last two years of study.
• The equivalent of a senior-level course in the area of theoretical computer science.
• Minimum English language test scores (if required): TOEFL(iBT) 90, IELTS 7, or York English Language Test 4.
• The Graduate Record Examination (GRE) general test is strongly recommended, especially for applicants who did their work outside of Canada and/or the United States.
• The equivalent of a senior-level course in the area of theoretical computer science.
• Minimum English language test scores (if required): TOEFL(iBT) 90, IELTS 7, or York English Language Test 4.
• The Graduate Record Examination (GRE) general test is strongly recommended, especially for applicants who did their work outside of Canada and/or the United States.
Degree Requirements
Three courses from the following list: EECS 5326, EECS 5327, EECS 6127, EECS 6327, EECS 6412
Two other courses from the following list: EECS 5323, EECS 5324, EECS 5326, EECS 5327, EECS 6127, EECS 6322, EECS 6323, EECS 6325, EECS 6327, EECS 6328, EECS 6332, EECS 6333, EECS 6340, EECS 6390A, EECS 6390D, EECS 6412, EECS 6414
PHIL 5340
Other Requirements
• A research project that applies AI to a practical problem under the supervision of faculty members and in collaboration with partners in the private or public sector
• At least one course must be from each of the following three Groups:
Group 1: Theory of Computing & Scientific Computing (EECS 6127)
Group 2: Artificial Intelligence & Interactive Systems (EECS 5323, EECS 5324, EECS 5326, EECS 5327, EECS 6322, EECS 6323, EECS 6325, EECS 6327, EECS 6328, EECS 6332, EECS 6333, EECS 6340, EECS 6390A, EECS 6390D)
Group 3: Systems: Hardware & Software (EECS 6412, EECS 6414)
• No more than two courses can be integrated with undergraduate courses (first digit is 5)
Three courses from the following list: EECS 5326, EECS 5327, EECS 6127, EECS 6327, EECS 6412
Two other courses from the following list: EECS 5323, EECS 5324, EECS 5326, EECS 5327, EECS 6127, EECS 6322, EECS 6323, EECS 6325, EECS 6327, EECS 6328, EECS 6332, EECS 6333, EECS 6340, EECS 6390A, EECS 6390D, EECS 6412, EECS 6414
PHIL 5340
Other Requirements
• A research project that applies AI to a practical problem under the supervision of faculty members and in collaboration with partners in the private or public sector
• At least one course must be from each of the following three Groups:
Group 1: Theory of Computing & Scientific Computing (EECS 6127)
Group 2: Artificial Intelligence & Interactive Systems (EECS 5323, EECS 5324, EECS 5326, EECS 5327, EECS 6322, EECS 6323, EECS 6325, EECS 6327, EECS 6328, EECS 6332, EECS 6333, EECS 6340, EECS 6390A, EECS 6390D)
Group 3: Systems: Hardware & Software (EECS 6412, EECS 6414)
• No more than two courses can be integrated with undergraduate courses (first digit is 5)
Students admitted to the Specialization in AI are in the position to apply for institutional and government scholarships, as well as the Vector Scholarships in Artificial Intelligence, valued at $17,500 for one year. No further financial support will be provided.
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AI Specialization Faculty
Robert Allison
Vision and intelligent interfaces
Aijun An
Website
Data mining, machine learning, information retrieval, and AI
Michael Brown
Computer vision and AI
Natalija Vlajic
Machine learning in computer security
Marcus Brubaker
Machine learning, probabilistic methods, computer vision and computational biology
Suprakash Datta
Machine learning for bioinformatics
Kosta Derpanis
Computer vision and machine learning
James Elder
AI and vision
Petros Faloutsos
AI for computer games and virtual humans
Gerd Grau
AI-based materials and process development
Michael Jenkin
Robotics and AI
Hui Jiang
Machine learning, speech and language processing, and computer vision
Ingo Fruend
Human vision and AI
Zhen Ming (Jack) Jiang
Software analytics and software performance engineering
Richard Wildes
AI and vision
Hossein Kassiri
AI-based algorithms for decoding a physiological/neurological function
Matthew Kyan
Machine intelligence approach for virtual environments
Yves Lesperance
Knowledge representation and reasoning, autonomous agents and multi-agent systems, and cognitive robotics
Peter Lian
Circuits and systems for embedded AI and neuromorphic computing
Marin Litoiu
Adaptive software and autonomic computing
Sebastian Magierowski
Hardware acceleration for machine learning
Manos Papagelis
Data mining, graph mining, machine learning, big data analytics, knowledge discovery
Ali Sadeghi-Naini
no link to personal webpage yet
AI in precision medicine; Machine learning in image-guided therapeutics; Quantitative imaging and radiomics
Mikhail Soutchanski
AI planning, planning in hybrid systems, knowledge representation including causality, reinforcement learning for planning
Pirathayini Srikantha
Game theory, large-scale optimization and distributed control for enabling adaptive, sustainable and resilient power grid operations
Zbigniew Stachniak
Automated reasoning and propositional satisfiability
Vassilios Tzerpos
Machine learning and audio
Ruth Urner
Machine learning theory
Franck van Breugel
Reinforcement learning for finding bugs in software