![\"\"](\"https:\/\/vieux.ivado.ca\/wp-content\/uploads\/2017\/10\/BanniereExcellenceScholarships.png\")
<\/h3>\nNext planned opening: Fall 2020<\/p>\n
<\/h3>\nIVADO\u2019s commitment to equity, diversity and inclusion and note to applicants<\/strong> <\/p>\n The goal of the excellence scholarship program is to support promising students in their training as future highly qualified personnel (researchers, professors, professionals) and more generally, future actors in the field of data science, mainly in IVADO members\u2019 areas of excellence: operations research, machine learning, decision sciences.<\/span><\/p>\n The funding period will start April 1st, 2020 or September 1st, 2020.<\/p>\n The funds shall be transferred to the office of research of the supervisor\u2019s university, and the university shall pay the student according to its own compensation rules. For projects that require ethics approval, the funds shall only be paid out once the approval is granted. Some projects may require specific agreements (e.g. pertaining to intellectual property).<\/span><\/p>\n Funding may be cut, withheld, delayed or rescinded under the circumstances outlined in the letter of award.<\/span><\/p>\n Review and criteria<\/strong><\/p>\n The applications shall be reviewed to ensure compliance with program rules (e.g. applications that are incomplete, exceed the page limit or list an ineligible applicant or supervisor). Only applications that meet all criteria will be forwarded to the review committee.<\/span><\/p>\n The parity-based review committee shall be made up of university professors and shall not be listed as a supervisor by any applicant. However, given the small size of the communities in certain areas, it may prove difficult to select expert reviewers who are not included in an application submitted to the competition. In such cases, a reviewer may be required to assess an application despite being listed in another application as a supervisor. An external reviewer may also join the committee. The committee shall ensure by all possible means that the reviewer does not influence the ranking of the application in which he\/she is included.<\/span><\/p>\n The review committee will check the project\u2019s alignment between the research project and IVADO\u2019s scientific direction, then shall rank the applications based on <\/span>excellence<\/b>, as well as the project\u2019s <\/span>alignment with IVADO\u2019s overarching framework<\/b>, which aims to promote multidisciplinary collaboration and diversity in data science.<\/span><\/p>\n In terms of excellence, the committee will specifically assess:<\/span><\/p>\n Final step and commitments<\/strong><\/p>\n The student shall:<\/span><\/p>\n The supervisor shall:<\/span><\/p>\n Didn\u2019t find what you were looking for? If not, send us an e-mail<\/a>.<\/p>\n<\/div> Please apply through: https:\/\/ivado.smapply.io\/<\/a><\/strong><\/p>\n All applications sent by email will be rejected.<\/p>\n All applications must contain:<\/p>\n Francis Banville<\/strong> (Universit\u00e9 de Montr\u00e9al, Timoth\u00e9e Poisot)<\/p>\n Avishek Bose<\/strong> (McGill University, William Hamilton)<\/p>\n Lluis E. Castrejon Subira<\/strong> (Universit\u00e9 de Montr\u00e9al, Aaron Courville)<\/p>\n Elodie Deschaintres<\/strong> (Polytechnique Montr\u00e9al, Catherine Morency)<\/p>\n Laura Gagliano<\/strong> (Polytechnique Montr\u00e9al, Mohamad Sawan)<\/p>\n Ellen Jackson<\/strong> (Universit\u00e9 de Montr\u00e9al, H\u00e9l\u00e8ne Carabin)<\/p>\n Mengying Lei<\/strong> (McGill University, Lijun Sun)<\/p>\n Tegan Maharaj<\/strong> (Polytechnique Montr\u00e9al, Christopher Pal)<\/p>\n Antoine Prouvost<\/strong> (Polytechnique Montr\u00e9al, Andrea Lodi)<\/p>\n Matthew Schlegel<\/strong> (University of Alberta, Martha White)<\/p>\n
\nTo ensure all members of society draw equal benefit from the advancement of knowledge and opportunities in data science, IVADO promotes equity, diversity and inclusion through each of its programs. IVADO aims to provide a recruitment process and research setting that are inclusive, non-discriminatory, open and transparent.<\/p>\nOverview<\/span><\/h4>
Description<\/span><\/h4>
FAQ<\/span><\/h4>
Application<\/span><\/h4>
Results - 2018 Contest<\/span><\/h4>
Results - 2019 Contest<\/span><\/h4>
Results - 2020 Contest<\/span><\/h4><\/div>
Program description<\/h3>\n
\n
\n<\/span><\/li>\n
\n<\/span><\/li>\nProgram objectives<\/span><\/h3>\n
Eligibility<\/h3>\n
\n
\n
\n
Funding period<\/h3>\n
Amounts and terms <\/h3>\n
Competitive process <\/h3>\n
\n
\n
\n
\n
FAQ<\/h3>\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\nWe humans are able to continually learn throughout our lifetime which is called lifelong learning. This capability is also crucial for computational systems interacting in the real world and processing continuous streams of data. However, the current deep learning systems struggle to continually acquire the incremental information available over time from non-stationary data distributions. They tend to forget the knowledge which is acquired earlier upon learning the new one – such a problem is called catastrophic forgetting. In this project, we will study biological factors of lifelong learning and their implications for the modelling of biologically motivated neural network architectures that can improve life-long learning capability of computational systems by reducing catastrophic forgetting problem.<\/li>\n<\/ul>\n<\/li>\n\n
\nArtificial Intelligence (AI) has an increasing impact on our everyday life, one being in health care. Today most of the successful applications of AI in healthcare are for diagnosis or prediction, but not for the treatment. But AI agents also have the potential for sequential decision making such as assisting doctors in reassessing treatment options, as well as in surgery. The branch of AI that is a natural fit for handling such sequential decision-making problems is known as Reinforcement Learning (RL).So far most of the successful applications of RL have been in the video game environments. But there are relatively fewer applications of RL in healthcare. One of the reasons is that unlike games, in healthcare the RL agents cannot interact with the environment to explore new possibilities to learn the optimal treatment policy. Trying new treatment options on patients without knowing their consequences is not only unethical but also can be fatal. So, the agent has to learn retrospectively from previously collected batches of data. In RL literature, this is called Off-Policy Learning. Challenges in off-policy evaluation, sparse reward, non-stationary data, and sample inefficiency are some of the roadblocks for using RL safely and successfully in healthcare. During my Ph.D. I aim to tackle some of these challenges in the context of healthcare.<\/li>\n<\/ul>\n<\/li>\n\n