Electrical Engineering and Computer Science

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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.

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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.
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)
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

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