Collaborative Specialization in Machine Learning in Health and Biomedical Sciences

Biomedical research, public health policy, and medical practice all increasingly rely on large sets of genetic, epidemiological, physiological, imaging, and/or behavioral data. This requires the development and application of modern machine learning, artificial intelligence, and statistical techniques that are suited to the problem at hand. To be successful in this rapidly evolving field, graduates need to be equipped both with expertise in machine learning methods, as well as a thorough subject-specific knowledge in the application area.

There are many challenges and opportunities for machine learning in the biomedical sciences that deserve special attention. The confidentiality of medical records, ethical implications of using data science methods for medical decision making, implementation of advanced technologies and analytics in healthcare settings, acceptance of health technologies and machine learning by diverse communities, and specific data formats for biomedical applications are only a few examples.

To provide world-class graduate training for this rapidly growing sector, several graduate programs across the Faculties of Science, Health Sciences, Engineering, and the Schulich School of Dentistry and Medicine have developed a Collaborative Specialization that provides targeted and practice-oriented training.

The program is open for PhD students and Masters students on a thesis-based track in the following programs:

  • Biomedical Engineering (MESc & PhD)
  • Medical Biophysics (MSc & PhD
  • Neuroscience (MSc & PhD)
  • Computer Science (MSc & PhD)
  • Mechanical and Materials Engineering (MESc & PhD)
  • Epidemiology and Biostatistics (MSc & PhD)
  • Electrical and Computer Engineering (MESc & PhD)
  • Physics (MSc & PhD)
  • Health and Rehabilitation Sciences (MSc & PhD)
  • Psychology (MSc & PhD)
  • Biochemistry (MSc & PhD)
  • Anatomy and Cell Biology( MSC & PhD)

This program enhances the education and research of a graduate student in one of the participating programs by adding a module to their program. The student receives a degree from his or her home department program along with a transcript notation Machine Learning in Health and Biomedical Sciences., e.g. MESc in Biomedical Engineering with specialization in Machine Learning in Health and Biomedical Sciences.

Students in the program will:

  • develop a solid foundation in modern machine learning techniques,
  • gain a deep understanding of the application of machine learning to genomic, physiological, imaging, and behavioral data,
  • conduct an independent research project in health and biomedical science using modern machine learning approaches,
  • learn how to effectively work inter-disciplinary teams,
  • acquire project management and entrepreneurial skills, and
  • learn how to consider societal and ethical implications in project design.


Additional to all requirements of the home graduate program, all students are required to take the following classes. Note that some of these courses can fulfill the requirement of electives in the home program. Please inquire with the individual graduate program chair.

  • DS 9000: Intro to Machine Learning, or a similar foundational ML class
  • At least one of the applied machine learning courses:
    • AM 9624B: Introduction to Neural Networks (also Psych9221B)
    • BME 9709: Biomedical Applications of Neural Networks (Biophys 9709B)
    • CS 9542B: Artificial Intelligence II
    • CS 9873B: Brain Inspired AI
  • additional courses on wearable sensors and genomics are likely to be added in the future
  • DS 9600A: Machine Learning in Health and Biomedical Sciences. Core seminar covering ethics, communication, and project management aspects.

All students are also required to complete an MSc or PhD thesis in their home program. The thesis topic needs to be in the domain of the collaborative specialization. An application of machine learning to a specific Health or Biomedical problem, or the development of a new method that is of especial interest to such problems would fulfill this requirement. Before admission to the collaborative specialization, the thesis topic needs to be submitted to the program committee for approval.

Admission into the Collaborative Specialization

You must first be accepted into one of the listed graduate programs above.

New graduate students (Masters/PhD) starting Fall 2022 or later are eligible to apply for admission. PhD students that started their degree no earlier than Sept 1, 2020 are also eligible to apply. Deadline for application is June 1 for Fall admissions; Oct 1 for January Admissions; and Feb 1 for Summer admission. **Exception: for the January 2023 term, the deadline for application is extended to December 1, 2022.** 

Seek admission to the collaborative specialization by submitting a description of your thesis project. 

Please see the Collaborative Specialization in Machine Learning in Health and Biomedical Sciences, Handbook (2022-2023) for more complete details on the program. 

Faculty supervisors of students in the specialization must also seek membership, see the handbook above for more information. See the current list of Faculty members in the Collaborative Specialization.

The Collaborative Specialization in Machine Learning in Health and Biomedical Sciences is recognized by the Vector Institute as delivering a curriculum that equips its graduates with the skills and competencies sought by industry. Students in the Collaborative Specialization become part of the Vector Institute community and receive access to networking and career events, the Digital Talent Hub, professional development, and other opportunities to grow your AI career!

Vector Institute Scholarship

Vector Scholarship in Artificial Intelligence:

Exceptional students may be eligible for the Vector Scholarship in Artificial Intelligence, which provides $17,500 towards an AI-related master’s degree at an Ontario university. Both domestic and international students with first-class standing (minimum A- in their last two years of full-time study) are eligible for consideration. Students with an upper second class standing (B+), who also have relevant work experience may be considered.

In order to be nominated, candidates must submit a complete nomination package to the Collaborative Specialization (instructions below) by no later than March 1, 2023 (11:59 PM EST).  Late applications will not be accepted. Letters of reference must also be received by this date. The committee will review all applications for their funding competitiveness and suitability.  Submitting an application does not guarantee a nomination for this award. Nomination packages will be submitted no later than March 22, 2023.  Learn more about the scholarship and eligibility requirements at or sign up to hear directly from Vector at

The full application packet for 2023-2024 will become available January 3, 2023 and will be accessible at Sample forms from previous competitions are provided below, but applicants MUST submit the 2023-2024 package forms to be considered.

The Vector Scholarship Nomination package should include:

  1. Copies of all up-to-date transcripts with the corresponding grading system (both undergraduate, and if applicable, graduate transcripts), as a single PDF
  2. An up-to-date one to two-page CV, as a single PDF
  3. A 250-word statement outlining their reason for pursuing a master’s in AI, relevant AI-related experience, and career aspirations, as a single PDF (see Student Statement Template PDF)
  4. Self-Identification Questionnaire (see Questionnaire PDF form)
  5. Referee forms are to be sent by e-mail to the Program at, by March 1, 2023.

You must submit parts 1 through 4 of the nomination package by our new deadline, March 1, 2023, 11:59 pm at this Dropbox link. (Do not e-mail your nomination package)

Contact information

Co-directors of the program can be reached at

Ali Khan, PhD  
Associate Professor, Medical Biophysics  
Co-Director, Collaborative Specialization in ML-HealthBiomed  

Angela Roberts, PhD  
Assistant Professor, School of Communication Sciences and Disorders and Department of Computer Science  
Co-Director, Collaborative Specialization in ML-HealthBiomed