IVADO funding program for excellence scholarships – Msc
IVADO’s commitment to equity, diversity and inclusion and note to applicants
To 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.
Results - 2018 Contest
- Field of study: The IVADO Scholarship Funding program supports research on the issues raised in the Canada First funding competition: data science in a broad sense, including methodological research in data science (machine learning, operations research, statistics) and its application in a range of sectors including the priority sectors of IVADO (health, transportation, logistics, energy, business and finance) or any other sector of application (sociology, physics, linguistics, engineering, etc.).
- Amount of award and grant period: 20k$ per year for a maximum of 6 sessions or 2 years
- Application deadline: TBD
- Expected application notification date: TBD
- Criteria: See the description tab
- Submission: See the submission tab
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’ areas of excellence: operations research, machine learning, decision sciences.
- Scholarship applicants must:
- have already earned their master degree prior to the date on which they are applying or intend to earn it by the date on which the competition results are announced. IVADO will be flexible with applicants who provide an adequate explanation for a career interruption or particular circumstances. This explanation must be included in the application (e.g. pregnancy/maternity or sick leave);
- intend to attend HEC Montréal, Polytechnique Montréal, Université de Montréal, McGill University or University of Alberta;
- have a first class minimum average grade (3.7/4.3 or 3.5/4.00) over the previous years of study.
- Professor (supervisor) applicants must:
- hold a faculty position as a professor at HEC Montréal, Polytechnique Montréal or Université de Montréal;
- Professors at the University of Alberta and McGill University may act as supervisors providing they are members of IVADO (MILA, CIRRELT, GERAD, CERC Data Science, CRM, Tech3Lab).
- Only submit one application to the competition.
The funding period starts in April 2019.
Amounts and terms
The funds shall be transferred to the office of research of the supervisor’s university, and the university shall pay the postdoctoral researcher 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).
Funding may be cut, withheld, delayed or rescinded under the circumstances outlined in the letter of award.
Review and criteria
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 the applications that meet all criteria will be forwarded to the review committee.
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.
The review committee will check the project’s alignment between the research project and IVADO’s scientific direction, then shall rank the applications based on excellence, as well as the project’s alignment with IVADO’s overarching framework, which aims to promote multidisciplinary collaboration and diversity in data science. In terms of excellence, the committee will specifically assess:
In terms of excellence, the committee will specifically assess:
- Research ability
- Depth and breadth of experience: multidisciplinary and professional experiences, extra-academic activities, collaborations, contributions to the scientific community and society as a whole, etc.
- Expected adequacy with the proposed project
Final step and commitments
The student shall:
- be physically present at his/her supervisor’s university;
- contribute to IVADO’s community and activities by, for example, taking part in:
- presentations on his/her research;
- training and knowledge dissemination activities;
- activities generally undertaken by career researchers (mentorship, assessment, co-organization of events, etc.);
- recognize that he/she is a member of an academic community to which he/she shall contribute;
- comply with the Tri-Agency Open Access Policy on Publication. Postdoctoral researchers are encouraged to publish their research findings (papers, recordings of presentations, source codes, databases, etc.), in compliance with the intellectual property rules that apply to their own specific case;
- recognize the financial support granted by IVADO and the CFREF or FRQ when disseminating the research results and, more broadly, in all the activities in which he/she takes part
The supervisor shall:
- provide a work environment that is conducive to the completion of the project
- oversee the work of the student
- Is there a particular format for preparing a CV?
- No, there is no particular format that needs to be followed. However, each piece of the record must help the assessor to form an opinion on the record. A CV that is too long or confusing may make evaluation more difficult.
- Are there any specific rules for the recommendation letter?
- No, there are no specific rules for the recommendation letter.
- Can candidates send recommendation letters themselves?
- No, recommendation letters can only be upload by their author in the platform.
- Can I send my unofficial transcript?
- No, you must upload us your official transcript including all your current results. Originals or certified copies must be scanned and uploaded to the application and for non-Canadian universities, you must specify the rating scale.
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Application through the platform
Link available in January 2019.
All applications will contain:
- a questionnaire to be completed on the platform WITH a description of the project (maximum length of one page);
- CV (free format) to be uploaded;
- Master transcripts (as well as information on the grading scale when the transcript is issued by a non-Canadian university);
- recommendations (a maximum of 2), including an uploaded letter directly from the future supervisor.
- Larry Dong (McGill University, Erica Moodie)
- When making decisions, medical professionals often rely on past experience and their own judgment. However, it is often the case that an individual decision-makerfaces a situation that is unfamiliar to him or her. An adaptive treatment strategy (ATS) can help such biomedical experts in their decision-making, as they are a statistical representation of a decision algorithm for a given treatment that optimizes patient outcomes. ATSs are estimated with large amounts of data, but an issue that may occur is that such sources of data may be subject to unmeasured confounding, whereby important variables needed to ensure the causal inference are missing. The idea behind this research project is to develop a sensitivity analysis to better understand and to quantify the impact of unmeasured confounding on decision rules in ATSs.
- Jonathan Pilault (Polytechnique Montréal, Christopher Pal)
- Language understanding and generation is a unique capacity of humans. Automatic summarization is an important task in Natural (human) Language Processing. This task consists in reducing the size of discourse while preserving information content. Abstractive summarization sets itself apart from other types of summarization since it most closely relates to how humans would summarize a book, a movie, an article or a conversation. From a research standpoint, automatic abstractive summarization is interesting since it requires models to both understand and generate human language. In the past year, we have seen research that have improved the ability of Neural Networks to choose the most important parts of discourse while beginning to address key pain points (e.g. repeating sentences, nonsensical formulations) during summary text generation. Recent techniques in Computer Vision image generation tasks have shown that image quality can be further improved using Generative Adversarial Networks (GAN). Our intuition is that the same is true for a Natural Language Processing task. We propose to incorporate newest GAN architectures into some of the most novel abstractive summarization models to validate our hypothesis. The objective is to create a state-of-the-art summarization system that most closely mimics human summarizers. This outcome will also bring us closer to understand GANs analytically.
- Alice Wu (Polytechnique Montréal, François Soumis)
- Combiner l’A.I. et la R.O. pour optimiser les blocs mensuels d’équipages aérien.Nos travaux récents portent sur le développement de deux nouveaux algorithmes Improved Primal Simplex (IPS) et Integral Simplex Using Decomposition (ISUD) qui profitent de l’information a priori sur les solutions attendues pour réduire le nombre de variables et de contraintes à traiter simultanément. Actuellement cette information est donnée par des règles fournies par les planificateurs. L’objectif de recherche sera de développer un système utilisant l’intelligence artificielle (IA) pour estimer la probabilité que la variable liant deux rotations fasse partie de la solution d’un problème de blocs mensuels d’équipages aériens. L’apprentissage se fera sur les données historiques de plusieurs mois, de plusieurs types d’avions et de plusieurs compagnies. L’estimation des probabilités doit se faire à partir des caractéristiques des rotations et non à partir de leurs noms. Une rotation ne revient pas d’une compagnie à l’autre ni d’un mois à l’autre. Il faudra identifier les caractéristiques pertinentes. Il faudra de la recherche sur l’apprentissage pour profiter des contraintes du problème. Il y a des contraintes entre le personnel terminant des rotations et celui en commençant par la suite. La validation de l’apprentissage se fera en alimentant les optimiseurs avec l’information estimée et en observant la qualité des solutions obtenues et les temps de calcul. Il y aura de la recherche à faire dans les optimiseurs pour exploiter au mieux cette nouvelle information.