Excellence Scholarships

IVADO funding program for excellence scholarships

The goal of the excellence scholarship program is to support promising students in their training as future highly qualified personnel and future actors in the fields promoted by IVADO.

Introduction

Program description

FAQ

Application

Results - 2018 Contest

Program description

  • Program Name: IVADO funding program for excellence scholarships
  • Program Type: Scholarships for graduate students
  • Strategic / priority domain: Data science, data-driven innovation.

Goals

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.

Deadlines

  • Application deadline: December 15th, 2017
  • Expected application notification date: April 1st, 2018
  • Official acceptance from the candidate before May 1st, 2018
  • Grant should start within nine months of announcement, and can be retroactive with some conditions.

Research area supported

The excellence scholarship program supports research concerning the issues described in the Apogée/CFREF grant (http://www.cfref-apogee.gc.ca/results-resultats/abstracts-resumes/competition_2/universite_de_montreal-eng.aspx): data science in the broadest sense, including methodological research in data science (machine learning, operations research, statistics) and their applications in various fields (including health, transportation, logistics, energy, business and finance).

Program description

  • Program Name: IVADO funding program for excellence scholarships
  • Program Type: Scholarships for graduate students
  • Strategic / priority domain: Data science, data-driven innovation.

Goals

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.

Deadlines

  • Application deadline: December 15th, 2017
  • Expected application notification date: April 1st, 2018
  • Official acceptance from the candidate before May 1st, 2018
  • Grant should start within nine months of announcement, and can be retroactive with some conditions.

Research area supported

The excellence scholarship program supports research concerning the issues described in the Apogée/CFREF grant (http://www.cfref-apogee.gc.ca/results-resultats/abstracts-resumes/competition_2/universite_de_montreal-eng.aspx): data science in the broadest sense, including methodological research in data science (machine learning, operations research, statistics) and their applications in various fields (including health, transportation, logistics, energy, business and finance).

Amount and duration of the scholarships

Available scholarships include:

  • For Master students: 4 scholarships at 20k$ per year (including social benefits/taxes), for a maximum of six sessions or two years
  • For Doctoral students: 6 scholarships at 25k$ per year (including social benefits/taxes), for a maximum of 12 sessions or four years

Funding will be renewed every year upon request from the supervisor(s):

  • Until a maximum of six sessions for Master students, 12 sessions for Doctoral students.
  • Until the diploma has been granted.

Students are allowed to reapply to the program at the end of their funding.

Guidelines

Generally, the Tri-Agency Financial Administration Guide (http://www.nserc-crsng.gc.ca/professors-professeurs/financialadminguide-guideadminfinancier/index_eng.asp) and the rules of the Apogée/CFREF program (http://www.cfref-apogee.gc.ca/program-programme/admin_guide-guide_administration-eng.aspx) will serve as guides for the program.

Eligibility

For the students:

  • MSc and PhD students, enrolled, or planning to enroll in a program at HEC Montréal, Polytechnique Montréal, Université de Montréal, McGill University or University of Alberta.
  • Must have a first class minimum average grade (3.7/4.3 or 3.5/4.00) over the previous years of study.

For the professor submitting the application (main supervisor)

  • Must be a professor from one of the institutions of Campus Montréal: HEC Montréal, Polytechnique Montréal or Université de Montréal.
  • Professors from the University of Alberta and University McGill can also be supervisors, provided that they are also members of one of IVADO’s research groups (MILA, CIRRELT, GERAD, CERC data science, CRM or Tech3Lab).

The student can be co-supervised, and there is no eligibility constraints on the co-supervisor.

For the activities planned for the duration of the scholarship:

  • activities must be aligned with the goals of IVADO and Apogée/CFREF.

Application

Elements of the application:

  • CV of the candidate
  • Grade transcript of previous sessions
  • CV of the supervisor(s)
  • Expected start date
  • A maximum of three support letters
  • Research project, co-written with the supervisor (1 p. max)
  • A paragraph with a popularized description of the research project. This text could be made public by IVADO

The application is presented by the supervisor. Each supervisor cannot present more than 5 candidates.

Assessment Criteria

The evaluation of projects will be based mainly on:

  • Candidate’s record
    • Research ability
    • Depth and breadth of experience (multidisciplinary, professional experience, extra-curricular activities…)
    • Expected adequacy with the proposed project

And secondarily on:

  • Scientific merit of the research project
    • Contribution to the field
    • Originality, anticipated impacts
    • Feasibility, relevance of the host laboratory(ies) and supervisor(s)
  • Suitability of the project within the overall framework of IVADO and Apogée/CFREF
    • Promoting multidisciplinary collaboration
    • Fostering diversity

Evaluation Process

Proposals will go through an administrative screening to check for program requirements (incomplete, too long, ineligible supervisor or candidate, etc.). Only applications meeting the requirements will be forwarded to the evaluation committee.

The evaluation committee will consist of faculty from Campus Montreal, knowledgeable about areas of excellence of IVADO, and who present no candidate. Depending on the situation, the committee could enlist the help of other local or external reviewers (professors or postdoctoral fellows).

The evaluation committee will first validate the scientific correspondence of the project with areas fostered by IVADO. All fitting applications will be ranked according to the criteria, and scholarships will be awarded following the ranking established.

Financing requirements

  • For projects requiring ethical approval, the funds will not be released until approval is obtained.
  • The funds will be transferred to the Office of Research of the institution of the supervisor, and the institution will administer it according to its own rules.
  • At the end of the funding period, the supervisor will not be eligible to apply for other IVADO funding programs until the final report is produced.

Commitments

  • The student agrees to be significantly present at his supervisor’s university.
  • The student intends to reasonably attend and participate in community/IVADO activities, including for example:
    • presentations on their own research
    • training or knowledge dissemination activities
    • counseling
    • support on various activities that are normally part of a research career (mentoring, participation in reviews, co-organizing events, etc.)
  • The student recognizes that he/she is part of an academic community, to which he/she will contribute.
  • The student will be subject to the policy of the three organizations on free access to publications (http://www.science.gc.ca/eic/site/063.nsf/fra/h_F6765465.html).They are encouraged to publish as much of their research productions (publications, recording oral presentations, source code, databases, etc.) in compliance with the rules of intellectual property that apply in their specific case.

The support of IVADO and Apogée/CFREF must be acknowledged in the dissemination of research results and more generally in the various activities that the student will participate in.

The supervisor commits to:

  • Providing a work environment that suits the completion of the project
  • Supervising the student.

Final report

At the end of the funding period, the supervisor and the student must submit a final report including:

  • project review;
  • list of publications;
  • list of participation in events;
  • list of participation in community/IVADO activities.

The supervisor cannot apply to other funding opportunities from IVADO as long as this report is due.

Contacts

FAQ

  • Question?
    • Answer!

Other questions? Please submit them to programmes-excellence@ivado.ca

The elements of the application are:

  • This form, filled.
  • CV of the candidate
  • CV of the supervisor(s)
  • A cover letter of the candidate (optional, max. one page)
  • Description of the project (max. one page)
  • A maximum of three support letters (there is no minimum)
  • Grade transcript of previous sessions

The application must be sent to programmes-excellence@ivado.ca, CCed to the supervisor and the candidate, preferably as a single pdf file. If prefered, support letters can be sent directly to   programmes-excellence@ivado.ca .

  • Chun Cheng (Polytechnique Montréal, Louis-Martin Rousseau)
    • Our project dedicates to deal with uncertainty in drone routing for disaster response and relief operations. To tackle the uncertainties arose from disaster scenarios, like uncertain demand locations and quantities for relief supplies, we use data-driven robust optimization (RO) method. This technique protects the decision makers against parameter ambiguity and stochastic uncertainty by using uncertainty sets. Therefore, it is significant to set proper parameters for the uncertainty set: a small set cannot accurately capture possible risks while a larger one may lead to overly conservative solutions. To address this problem, we use machine learning (ML) technique to extract information from historical data and real-time observations, and create the parameters by ML algorithms. After calibrating the uncertainty set, we will determine appropriate models for the problem by considering various theories in RO, such as static RO and multiple stage adjustable RO. These approaches will be measured against other applicable approaches such as stochastic programming.
  • 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.
  • Dominique Godin (Université de Montréal, Jean-François Arguin)
    • Ce projet de recherche a pour objectif de développer et mettre en application des techniques d’apprentissage machine afin de grandement améliorer l’identification des électrons par le détecteur ATLAS du LHC, le plus grand accélérateur de particules jamais construit et l’un des projets scientifiques les plus ambitieux de tous les temps.Afin de mener à bien le programme d’ATLAS, il est nécessaire d’identifier et mesurer chacune des particules, lesquelles s’y créer à un taux de 40 milliards par seconde et génèrent un flot astronomique de données. Parmi celles-ci, les électrons revêtent une très grande importance, mais ils sont également excessivement rares, ne représentant qu’une infime fraction. Considérant la taille et complexité des données disponibles, le problème d’identification des particules aussi rares que les électrons constitue un terrain d’application idéal pour les méthodes d’apprentissage machine. Les algorithmes actuels d’identification des électrons sont très simples et ne font pas usage de ces méthodes de telle sorte qu’une percée dans ce domaine serait une première mondiale qui pourrait éventuellement paver la voie à des découvertes majeures en physique des particules.
  • Charley Gros (Polytechnique Montréal, Julien Cohen-Adad)
    • Multiple sclerosis (MS) is a disease, with a high rate in Canada, that leads to major sensory and motor problems. This disease affects the neuronal signal transmission in both brain and spinal cord, creating lesions, which are observable on images acquired with an MRI scanner. The count and volume of lesions on an MRI scan of a patient is a crucial indicator of the disease status and commonly used by doctors for the diagnosis, prognosis and therapeutic drug trials. However, the detection of lesions is very challenging and time consuming for radiologists, due to the high variability of their size and shape.This project aims at developing a new, automatic and fast method of MS lesion detection on MRI data of spinal cord, based on newly developed machine learning algorithms. The new algorithm’s performance will be tested on a large dataset involving patients coming from different hospitals in the world. Once the algorithm is optimized, it will be freely available as part of an open-source software, already widely used for spinal cord MRI processing and analysis. A fundamental goal of this project is the integration of this algorithm in hospitals to help radiologists in their daily work.
  • 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.
  • Thomas Thiery (Université de Montréal, Karim Jerbi)
    • When we are walking through a crowd, or playing a sport, our brain continuously makes decisions about directions to go to, obstacles to avoid and information to pay attention to. Fuelled by the successful combinations of quantitative modeling and neural recordings in nonhuman primates, research into the temporal dynamics of decision-making has brought the study of decision-making to the fore within neuroscience and psychology, and has exemplified the benefits of convergent mathematical and biological approaches to understanding brain function. However, studies have yet to uncover the complex dynamics of large-scale neural networks involved in dynamic decision-making in humans. The present research aims to use advanced data analytics to identify the neural features involved in tracking the state of sensory evidence and confirming the commitment to a choice during a dynamic decision-making task. To this end, we will use cutting-edge electrophysiological brain imaging (magnetoencephalography, MEG), combined with multivariate machine learning algorithms. This project, for the first time, will shed light on the whole-brain large-scale dynamics involved in dynamic decision-making, thus providing empirical evidence that can be generalized across subjects to test and refine computational models and neuroscientific accounts of decision-making. By providing a quantitative link between the behavioral and neural dynamics subserving how decisions are continuously formed in the brain, this project will contribute to expose mechanisms that are likely to figure prominently in human cognition, in health and disease. Moreover, this research may provide neurobiological-inspired contributions to machine learning algorithms that implement computationally efficient gating functions capable of making decisions in a dynamically changing environment. ln addition to advancing our knowledge of the way human brains come to a decision, we also foresee long-term health implications for disorders such as Parkinson’s disease.
  • 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.