Fundamental research projects grants

The Fundamental Research Funding Program provides a framework for fostering multidisciplinary research in data science and by confirming and strengthening the IVADO community as a key player in the field. This program is also very focused on the future, encouraging the training of future data researchers and the creation of the scientific foundations of tomorrow’s fundamental and applied research.


Program description




Program description

  • Program name: IVADO funding program for fundamental research projects
  • Program type: Grant for multidisciplinary team
  • Type of research: basic / fundamental research
  • Strategic / priority domain : Data science, data-driven innovation


The goals of this program are to:

  • Promote multidisciplinary research in data science, primarily in IVADO’s areas of excellence: operation research, machine learning and decision sciences.
  • Develop the same areas of excellence internationally. Confirm and strengthen the position of IVADO members in these areas in Montreal, Quebec and Canada..
  • Train future key players in these areas of excellence.
  • Lay the groundwork for subsequent research, fundamental or applied.


  • Announcement: April 2017
  • Application deadline: June 15th, 2017
  • Expected application notification date: October 15th, 2017
  • Start date of funding: November 1st, 2017

Research area supported

The fundamental research funding program supports fundamental research concerning the issues described in the awarded Apogée/CFREF grant ( ): data science in the broadest sense, including methodological research in data science (machine learning, operation research, statistics) and their applications in various fields (including health, transportation, logistics, resources and energy, information and trade).

The research supported by this program must be collaborative and multidisciplinary, whether through several methodological approaches (machine learning, operational research, decision sciences) or through combining a methodological approach with an application field.


Program description

  • Program name: IVADO funding program for fundamental research projects
  • Program type: Grant for multidisciplinary team
  • Type of research: basic / fundamental researc
  • Strategic / priority domain : Data science, data-driven innovation


The goals of this program are to:

  • Promote multidisciplinary research in data science, primarily in IVADO’s areas of excellence: operation research, machine learning and decision sciences.
  • Develop the same areas of excellence internationally. Confirm and strengthen the position of IVADO members in these areas in Montreal, Quebec and Canada..
  • Train future key players in these areas of excellence.
  • Lay the groundwork for subsequent research, fundamental or applied.


  • Announcement: April 2017
  • Application deadline: June 15th, 2017
  • Expected application notification date: October 15th, 2017
  • Start date of funding: November 1st, 2017

Research area supported

The fundamental research funding program supports fundamental research concerning the issues described in the awarded Apogée/CFREF grant ( ): data science in the broadest sense, including methodological research in data science (machine learning, operation research, statistics) and their applications in various fields (including health, transportation, logistics, resources and energy, information and trade).

The research supported by this program must be collaborative and multidisciplinary, whether through several methodological approaches (machine learning, operational research, decision sciences) or through combining a methodological approach with an application field.

Available funds

For this first contest, around 10 projects will be selected for a typical funding between $ 50k and $ 100k per year for two years. The maximum allowable is $ 300k spread over two years. A portion of the total available budget will be allocated to student scholarships (graduate and undergraduate students) and postdoctoral fellows; applicants are encouraged to submit a budget in which at least two-thirds (66%) falls into this category.

Eligibility of applicants and co-applicants

  • The principal investigator 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 principal investigators, provided that they are also members of one of IVADO’s research groups (MILA, CIRRELT, GERAD or CERC data science).
  • The team must be composed of at least two professors. Postdocs are allowed to be members of the team.
  • Team members should not have all their primary affiliation in the same department.
  • The principal investigator can only apply on one application.


Generally, the Tri-Agency Financial Administration Guide ( ) and the rules of the Apogée/CFREF program ( ) will serve as guides for the program.

Eligible expenses

This program allows for the funding of:

  • graduate and undergraduate students;
  • postdoctoral researchers;
  • professionals;
  • travel expenses;
  • hardware, software, databases and access to computing resources.

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 principal investigator, and the institution will administer it according to its own rules.
  • At the end of funding, the principal investigator will not be eligible to apply to other IVADO funding programs until he or she produces a final report.

Availability of research data

  • Funded teams will be subject to the Tri-Agency Open Access Policy on Publications ( ). Teams 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.
  • The support of IVADO and Apogée/CFREF must be acknowledged in the dissemination of research results.


Administrative screening

Proposals will go through an administrative screening. The review committee will not receive applications :

  • if they do not meet the format constraints (missing sections, excessive number of pages, …);
  • that are not presented by an eligible professor;
  • if the same principal investigator presented multiple applications;
  • in which all team members are from the same department.

Evaluation criteria

The evaluation of projects will be based in equal parts on, the one hand:

  1. Research project
    • Relevance to the goals and domains of the funding program:
      • multidisciplinary project in the field of data science,
      • primarily in the areas of excellence of IVADO: operational research, machine learning, decision sciences.
    • Significance of anticipated contribution to knowledge, research excellence, originality.
    • Project feasibility, appropriateness of the methodology.
    • Clarity of the proposal.
    • Existence of a reasonable and well justified budget.

and on the other:

  1. Team and individuals
    • Excellence of the researchers involved in the application, according to their level of advancement in their career.
    • Justification of the team composition (match between the team and the project, multiplicity of expertise).
    • Presence of early career researchers (recently hired professors, postdoc researchers).
  2. Training opportunities
    • Involvement of students (undergraduate and graduate).
    • Involvement of post-doctoral researchers.
    • Hiring and training of research professionals.
  3. Consistency with the other objectives of Apogée/CFREF and IVADO
    • Presence in the team
      • of researchers outside of Montreal;
      • of postdoctoral researchers funded by IVADO;
      • of professors recruited under the IVADO program.
    • International collaborations.
    • Validity of the dissemination plan.
    • Integration in a wider research program and with longer term goals.
    • Efforts on the representativeness and diversity in the team composition or hiring intentions (women, minorities,…). Researchers are encouraged to work on the representativeness when building their team and during recruitment.


Each application will be reviewed by two local reviewers and two external reviewers, chosen from people knowledgeable in the domain of the application. Direct collaborators (people with a recent history of co-supervising, co-publishing, etc.) of team members will not participate in reviewing the application.

The evaluation committee consists of all the reviewers and, as observers, IVADO’s director of scientific programs and a representative of the Research Offices. This committee will evaluate and rank the proposals based on the evaluation criteria. This ranking will determine the funding of projects.

Funding requested might be altered based on availability of funds.

Each principal investigator will receive feedback from the evaluation committee.

Application process and elements

Instruction for submitting an application are available under the “Application” tab.

Elements of the application:

  • Name and affiliation of the principal investigator.
  • Name and affiliation of the other team members.
  • For each participant, a CV in an unconstrained format but listing at least the publications and activities of the last five years (Common CV Canada – NSERC is suggested).
  • List of keywords related to the application, specifying, among others, methodological areas, potential application areas, etc.
  • Justification of the team composition (max. ½ page).
  • Description of the research project (max. 4 pages).
  • Reference list (max. one page).
  • Requested budget, detailing:
    • scholarships for undergraduate and graduate students;
    • postdoctoral researchers;
    • professionals;
    • travelling expenses;
    • hardware, software, databases and access to computing resources.
  • Budget justification explaining the relationship between the research project and the funding requested (max. 2 pages).
  • Dissemination Plan (max. 1 page).
    • Publication goals, participation in conferences or other research dissemination events, organization of a local research day to present the outcomes of the project, workshop organization, …
  • HQP training plan (max. 1 page).
  • Four external reviewer options.

Final report

At the end of the two-year funding, the principal investigator must submit a final report including:

  • project review;
  • list of publications;
  • list of participations in events;
  • list of funded students, contact details and summary of their participation (in compliance with personal data management rules);
  • financial report;
  • list of the organized or co-organized knowledge dissemination activities;
  • new financing obtained or applied to on the basis of work carried out during this project.

The principal investigator can not apply to other funding opportunities from IVADO as long as this report is due.


  • Any questions concerning the funding program can be addressed to
  • Please also consult the FAQ section in this page.


Q: What happens if my application file does not meet the requested length?

R: Your application will automatically be disqualified.


Q: When will I get a reply on my application?

R: By October 15 (see section “Program description” for more details)


More questions? Please send them to:

The elements of the application are:

  • This form, filled.
  • CVs of the principal investigator and team members.
  • Justification of the team composition (max. ½ page).
  • Description of the project (max. 4 pages).
  • Reference list (max. 1 page).
  • Budget.
  • Budget justification (max. 2 pages).
  • Dissemination Plan (max. 1 page).
  • HQP training plan (max. 1 page).

The application must be sent to , CCed to the team members, preferably as a single pdf file.

  • Bram Adams (Polytechnique Montréal), Antoniol Giuliano, Jiang Zhen Ming & Sénécal Sylvain : A Real-time, Data-driven Field Decision Framework for Large-scale Software Deployments
    • As large e-commerce systems need to maximize their revenue, while ensuring customer quality and minimizing IT costs, they are constantly facing major field decisions like “Would it be cost-effective for the company to deploy additional hardware resources for our premium users?” This project will build a real-time, data-driven field decision framework exploiting customer behaviour and quality of service models, release engineering and guided optimization search. It will benefit both Canadian software industry and society, by improving the quality of service experienced by Canadians.
  • Jean-François Arguin (Université de Montréal), Tapp Alain, Golling Tobias, Ducu Otilia & Mochizuki Kazuya : Machine learning for the analysis of the Large Hadron Collider Data at CERN
    • The Large Hadron Collider (LHC) is one of the most ambitious experiment ever conducted. It collides protons together near the speed of light to reproduce the conditions of the Universe right after the Big Bang. It possesses all the features of Big Data: 1e16 collisions are produced each year, each producing 1000 particles and each of these particle leaving a complex signature in the 100 million electronic channels of the ATLAS detector. This project will initiate a collaboration between data scientists and physicists to develop the application of machine learning to the analysis of the LHC data.
  • Olivier Bahn (HEC Montréal), Caines Peter, Delage Erick, Malhamé Roland & Mousseau Normand : Valorisation des données et Optimisation Robuste pour guider la Transition Énergétique vers des réseauX intelligents à forte composante renouvelable (VORTEX)
    • Une modélisation multiéchelles consistant en une famille de modèles hiérarchisés et opérant à des échelles de temps croissantes (journée / semaine à mois / horizon de trente ans), et des outils mathématiques adaptés (jeux à champ moyen répétés, apprentissage machine, optimisation convexe et robuste), sont proposés comme base pour une gestion raisonnée de la transition vers des réseaux électriques intelligents à forte composante renouvelable. Notre projet proposera en particulier des outils pour aider à la maîtrise de la demande énergétique dans un contexte régional.
  • Yoshua Bengio (Université de Montréal), Cardinal Héloïse, Carvalho Margarida & Lodi Andrea : Data-driven Transplantation Science
    • End-stage kidney disease is a severe condition with a rising incidence, currently affecting over 40,000 Canadians.
      The decision to accept or refuse an organ for transplantation is an important one, as the donor’s characteristics are strongly associated with the long-term survival of the transplanted kidney. In partnership with their health care provider, the transplant candidates need to answer two questions: (1) How long is the kidney from this specific donor expected to last for me? (2) If I refuse this specific donor, how much longer am I expected to wait before getting a better kidney?
      We propose to use deep learning to predict the success of a possible matching. The results will contribute to build a clinical decision support tool answering the two questions above and helping transplant physicians and candidates to make the best decision. In addition, the quality of the matching can be the input of optimization algorithms designed to improve social welfare of organ allocations.
  • Michel Bernier (Polytechnique Montréal), Kummert Michaël & Bahn Olivier : Développement d’une méthodologie pour l’’utilisation des données massives issues de compteurs intelligents pour modéliser un parc de bâtiments
    • Les données disponibles grâce à la généralisation des compteurs communicants représentent une grande opportunité pour améliorer les modèles de parc de bâtiments et les modèles plus généraux de flux énergétiques, mais les connaissances fondamentales à ce sujet sont encore limitées. Le présent projet vise à y remédier en développant une méthodologie permettant d’’utiliser les données massives des compteurs électriques communicants pour caractériser et calibrer, notamment par modélisation inverse, des archétypes de bâtiments qui pourront être intégrés dans le modèle TIMES.
  • Guillaume-Alexandre Bilodeau (Polytechnique Montréal), Aloise Daniel, Pesant Gilles, Saunier Nicolas & St-Aubin Paul : Road user tracking and trajectory clustering for intelligent transportation systems
    • While traffic cameras are a mainstay of traffic management centers, video data is still most commonly watched by traffic operators for traffic monitoring and incident management. There are still few applications of computer vision in ITS, apart from integrated sensors for specific data extraction such as road users (RUs) counts. One of the most useful data to extract from video is the trajectory of all RUs, including cars, trucks, bicycles and pedestrians. Since traffic videos include many RUs, finding their individual trajectory is challenging. Our first objective is therefore to track all individual RUs. The second objective is to interpret the very large number of trajectories that can be obtained. This can be done by clustering trajectories, which provides the main motions in the traffic scene corresponding to RU activities and behaviors, along with their frequency or probability. Results of this research will be applicable for traffic monitoring in ITS and for self-driving cars.
  • François Bouffard (McGill University), Anjos Miguel & Waaub Jean-Philippe : The Electricity Demand Response Potential of the Montreal Metropolitan Community: Assessment of Potential Impacts and Options
    • This project will develop a clear understanding of the potential benefits and trade-offs of key stakeholders for deploying significant electric power demand response (DR) in the Montreal Metropolitan Community (MMC) area. It is motivated primarily by the desire of Hydro-Québec to increase its export potential, while at the same time by the need to assess DR deployment scenarios and their impacts on the people and businesses of the MMC. Data science is at the heart of this work which will need to discover knowledge on electricity consumption in order to learn how to leverage and control its flexibility.
  • Tolga Cenesizoglu (HEC Montréal), Grass Gunnar & Jena Sanjay : Real-time Optimal Order Placement Strategies and Limit Order Trading Activity
    • Our primary objective is to identify how institutional investors can reduce their risk and trading costs by optimizing when and how to execute their trades. Limit order trading activity is an important state variable for this optimization problem in today’s financial markets where most liquidity is provided by limit orders. We thus plan to first analyze how risk and trading costs are affected by limit order trading activity using a novel, large-scale, ultra-high-frequency trading data set. We will then use our findings to guide us in modeling these effects and devising real-time optimal order placement strategies.
  • Laurent Charlin (HEC Montréal) & Jena Sanjay Dominik : Exploiting ML/OR Synergies for Assortment Optimization and Recommender Systems
    • We propose to exploit synergies between assortment optimization and recommender systems on the application level, and the interplay between machine learning and mathematical programming on the methodological level. Rank-based choice models, estimated in a purely data-driven manner will introduce diversity into recommender systems, and supervised learning methods will improve the scalability and efficiency of assortment optimization in retail.
  • Julien Cohen (Polytechnique Montréal), Kadoury Samuel, Pal Chris, Bengio Yoshua, Romero Soriano & Guilbert François : Transformative adversarial networks for medical imaging applications
    • Following the concept of Generative adversarial networks (GANs), we propose to explore transformative adversarial training techniques where our goal is to transform medical imaging data to a target reference space as a way of normalizing them for image intensity, patient anatomy as well as the many other parameters associated with the variability inherent to medical images. This approach will be investigated both for data normalization and data augmentation strategy, and will be tested in several multi-center clinical data for lesion segmentation and/or classification (diagnosis).
  • Patrick Cossette (Université de Montréal), Bengio Yoshua, Laviolette François & Girard Simon : Towards personalized medicine in the management of epilepsy: a machine learning approach in the interpretation of large-scale genomic data
    • To date, more than 150 epilepsy genes have been identified explaining around 35% of the cases. However, conventional genomics methods have failed to explain the full spectrum of epilepsy heritability, as well as antiepileptic drug resistance. In particular, conventional studies lack the ability to capture the full complexity of the human genome, such as interactions between genomic variations (epistasis). In this project, we will investigate how we can use machine learning algorithms in the analyses of genomic data in order to detect multivariate patterns, by taking advantage of our large dataset of individual epilepsy genomes. In this multi-disciplinary project, neurologists, geneticists, bio-informaticians and computational scientists will join forces in order to use machine learning algorithms to detect genomic variants signatures in patients with pharmaco-resistant epilepsy. Having the ability to predict pharmaco-resistance will ultimately reduce the burden of the disease.
  • Benoit Coulombe (Université de Montréal), Lavallée-Adam Mathieu, Gauthier Marie-Soleil, Gaspar Vanessa, Pelletier Alexander, Wong Nora & Christian Poitras : A machine learning approach to decipher protein-protein interactions in human plasma
    • Proteins circulating in the human bloodstream make very useful and accessible clinical biomarkers for disease diagnostics, prognostics and theranostics. Typically, to perform their functions, proteins will interact with other molecules, including other proteins. These protein-protein interactions provide valuable insights into a protein’s role and function in humans; it can also lead to the discovery of novel biomarkers for diseases in which the protein of interest is involved. However, the identification of such interactions in human plasma is highly challenging. The lack of proper biochemical controls, which are inherently noisy, makes the confidence assessment of these interactions very difficult. We therefore propose to develop a novel machine learning approach that will extract the relevant signal from noisy controls to confidently decipher the interactome of clinically-relevant proteins circulating in the human bloodstream with the ultimate goal of identifying novel biomarkers.
  • Michel Denault (HEC Montréal), Côté Pascal & Orban Dominique : Simulation and regression approaches in hydropower optimization
    • We develop optimization algorithms based on dynamic programming with simulations and regression, essentially Q-learning algorithms. Our main application area is hydropower optimization, a stochastic control problem where optimal releases of water are sought at each point in time.
  • Michel Desmarais (Polytechnique Montréal), Charlin Laurent & Cheung Jackie C. K : Matching individuals to review tasks based on topical expertise level
    • The task of selecting an expert to review a paper addresses the general problem of finding a match between a human and an assignment based on the quality of expertise alignment between the two. State of the art approaches generally rely on modeling reviewers as a distribution of topic expertise, or as a set of keywords. Yet, two expert can have the same relative topic distribution and have wide differences in their depth of understanding. A similar argument can be made for papers. The objective of this proposal is to enhance the assignment approach to include the notions of (1) reviewer mastery of a topic, and (2) paper topic sophistication. Means to assess each aspect are proposed, along with approaches to assignments based on this additional information.
  • Georges Dionne (HEC Montréal) Morales Manuel, d’Astous Philippe, Yergeau Gabriel, Rémillard Bruno & Shore Stephen H. : Asymmetric Information Tests with Dynamic Machine Learning and Panel Data
    • To our knowledge, the econometric estimation of dynamic panel data models with machine learning is not very developed and tests for the presence of asymmetric information in this environment are lacking. Most often, researchers assume the presence of asymmetric information and propose models (sometimes dynamic) to reduce its effects but do not test for residual asymmetric information in final models. Potential non-optimal pricing of financial products may still be present. Moreover, it is often assumed that asymmetric information is exogenous and related to unobservable agent characteristics (adverse selection) without considering agents’ dynamic behavior over time (moral hazard). Our goal is to use machine learning models to develop new tests of asymmetric information in large panel data sets where the dynamic behavior of agents is observed. Applications in credit risk, high frequency trading, bank securitization, and insurance will be provided.
  • Marc Fredette (HEC Montréal), Charlin Laurent, Léger Pierre-Majorique, Sénécal Sylvain, Courtemanche François, Labonté-Lemoyne Élise & Karran Alexander : Improving the prediction of the emotional and cognitive experience of users (UX) in interaction with technology using deep learning.
    • The objective of this research project is to leverage new advances in artificial intelligence, and more specifically deep learning approaches, to improve the prediction of emotional and cognitive experience of users (UX) in interaction with technology. What users experience emotionally and cognitively when interacting with an interface is a key determinant of the success or failure of digital products and services. Traditionally, user experience has been assessed with post hoc explicit measures, (i.e. such as questionnaires. However, these measures are unable to capture the states of users while they interact with technology. Researchers are turning to neuroscience implicit measures to capture the user’s states through psychophysiological inference. Deep learning has recently enabled other fields such as image recognition to make significant progress and we expect that it will do the same for psychophysiological inference, allowing the automatic modeling of complex feature sets.
  • Geneviève Gauthier (HEC Montréal), Amaya Diego, Bégin Jean-François, Cabeda Antonio & Malette-Campeau : L’utilisation des données financières à haute fréquence pour l’estimation de modèles financiers complexes
    • Les modèles de marché permettant de reproduire la complexité des interactions entre l’actif sous-jacent et les options requièrent une complexité qui rend leur estimation très difficile. Ce projet de recherche propose d’utiliser les données financières d’options à haute fréquence afin de mieux mesurer et gérer les différents risques du marché.
  • Michel Gendreau (Polytechnique Montréal), Potvin Jean-Yves, Aloise Daniel & Vidal Thibaut : Nouvelles approches pour la modélisation et la résolution de problèmes de livraisons à domicile.
    • Ce projet porte sur le développement de nouvelles approches permettant de mieux aborder les problèmes de livraisons à domicile qui, suite à l’avènement généralisé du commerce électronique, ont connu un essor très important au cours de la dernière décennie. Une partie des travaux portera sur la modélisation même de ces problèmes, notamment en ce qui concerne les objectifs poursuivis par les expéditeurs. Le reste du projet visera sur le développement d’’heuristiques et de méta-heuristiques à la fine pointe des connaissances pour la résolution efficace de problèmes de grande taille.
  • Bernard Gendron (Université de Montréal), Crainic Teodor Gabriel, Jena Sanjay Dominik & Lacoste-Julien Simon : Optimization and machine learning for fleet management of autonomous electric shuttles
    • Recently, a Canada-France team of 11 researchers led by Bernard Gendron (DIRO-CIRRELT, UdeM) has submitted an NSERC-ANR strategic project “Trustworthy, Safe and Smart EcoMobility-on-Demand”, supported by private and public partners on both sides of the Atlantic: in Canada, GIRO and the City of Montreal; in France, Navya and the City of Valenciennes. The objective of this project is to develop optimization models and methods for planning and managing a fleet of autonomous electric shuttle vehicles. As a significant and valuable additional contribution to this large-scale project, we plan to study the impact of combining optimization and machine learning to improve the performance of the proposed models and methods.
  • Julie Hussin (Université de Montréal), Gravel Simon, Romero Adriana & Bengio Yoshua : Deep Learning Methods in Biomedical Research: from Genomics to Multi-Omics Approaches
    • Deep learning approaches represent a promising avenue to make important advances in biomedical science. Here, we propose to develop, implement and use deep learning techniques to combine genomic data with multiple types of biomedical information (eg. other omics datasets, clinical information) to obtain a more complete and actionable picture of the risk profile of a patient. In this project, we will be addressing the important problem of missing data and incomplete datasets, evaluating the potential of these approaches for prediction of relevant medical phenotypes in population and clinical samples, and developing integration strategies for large heterogeneous datasets. The efficient and integrated use of multiomic data could lead to the improvement of disease risk and treatment outcome predictions in the context of precision medicine.
  • Sébastien Jacquemont (Université de Montréal), Labbe Aurélie, Bellec Pierre, Catherine Schramm, Chakravarty Mallar & Michaud Jacques : Modeling and predicting the effect of genetic variants on brain structure and function
    • Neurodevelopmental disorders (NDs) represent a significant health burden. The genetic contribution to NDs is approximately 80%. Whole genome testing in pediatrics is a routine procedure and mutations contributing significantly to neurodevelopmental disorders are identified in over 400 patients every year at the Sainte Justine Hospital. However, the impact of these mutations on cognition and brain structure and function is mostly unknown. However, mounting evidence suggests that genes that share similar characteristics produce similar effects on cognitive and neural systems.
      Our goal: Develop models to understand the effects of mutations, genome-wide, on cognition, brain structure and connectivity.
      Models will be developed using large cohorts of individuals for whom, genetic, cognitive and neuroimaging data was collected.
      Deliverable: Algorithms allowing clinicians to understand the contribution of mutations to the neurodevelopmental symptoms observed in their patients.
  • Karim Jerbi (Université de Montréal), Hjelm Devon, Plis Sergey, Carrier Julie, Lina Jean-Marc, Gagnon Jean-François & Dr Pierre Bellec : From data-science to brain-science: AI-powered investgation of the neuronal determinants of cognitive capacities in health, aging and dementia
    • Artificial intelligence is revolutionizing science, technology and almost all aspects of our society. Learning algorithms that have shown astonishing performances in computer vision and speech recognition are also expected to lead to qualitative leaps in biological and biomedical sciences. In this multi-disciplinary research program, we propose to investigate the possibility of boosting information yield in basic and clinical neuroscience research by applying data-driven approaches, including shallow and deep learning, to electroencephalography (EEG) and magnetoencephalography (MEG) data in (a) healthy adults, and aging populations (b) with or (c) without dementia. The proposal brings together several scientists with expertise in a wide range of domains, ranging from data science, mathematics and engineering to neuroimaging, systems, cognitive and clinical neuroscience.
  • Philippe Jouvet (Université de Montréal), Emeriaud Guillaume, Michel Desmarais, Farida Cheriet & Noumeir Rita : Clinical data validation processes: the example of a clinical decision support system for the management of Acute Respiratory Distress Syndrome (ARDS)
    • In healthcare, data collection has been designed to document clinical activity for reporting, rather than for developing new knowledge. In this proposal, part of a research program on clinical decision support systems in real time in critical care, machine learning researchers and clinicians plan to generate algorithms to manage data corruption and data complexity using a unique research dataware house collecting hudge critically ill children data.
  • Aurelie Labbe (HEC Montréal), Larocque Denis, Charlin Laurent & Miranda-Moreno : Data analytics methods for travel time estimation in transportation engineering
    • Travel time is considered as one of the most important performance measures in urban mobility. It is used by both network operators and drivers as an indicator of quality
      of service or as a metric influencing travel decisions. This proposal tackles the issue of travel time prediction from several angles: i) data pre-processing (map-matching), ii) short-term travel time prediction and iii) long-term travel time prediction. These tasks will require the development of new approaches in statistical and machine learning to adequately model GPS trajectory data and to quantify the prediction error.
  • Frederic Leblond (Polytechnique Montréal), Trudel Dominique, Ménard Cynthia, Saad Fred, Jermyn Michael & Grosset Andrée-Anne : Machine learning technology applied to the discovery of new vibrational spectroscopy biomarkers for the prognostication of intermediate-risk prostate cancer patients
    • Prostate cancer is the most frequent cancer among Canadian men, with approximately 25,000 diagnoses per year. Men with high risk and low risk disease almost always experience predictable disease evolution allowing optimal treatment selection. However, none of the existing clinical tests, imaging techniques or histopathology methods can be used to predict the fate of men with intermediate-risk disease. This is the source of a very important unmet clinical need, because while some of these patients remain free of disease for several years, in others cancer recurs rapidly after treatment. Using biopsy samples in tissue microarrays from 104 intermediate-risk prostate cancer patients with known outcome, we will use a newly developed Raman microspectroscopy technique along with machine learning technology to develop inexpensive prognostic tests to determine the risk of recurrence allowing clinicians to consider more aggressive treatments for patients with high recurrence risk.
  • Pierre L’Ecuyer (Université de Montréal), Devroye Luc & Lacoste-Julien Simon : Monte Carlo and Quasi-Monte Carlo Methods for Optimization and Machine Learning
    • The use of Monte Carlo methods (aka, stochastic simulation) has grown tremendously in the last few decades. They a now a central ingredient in many areas, including computational statistics, machine learning, and operations research. Our aim in this project is to study Monte Carlo methods and improve their efficiency, with a focus on applications to statistical modeling with big data, machine learning, and optimization. We are particularly interested in developing methods for which the error converges at a faster rate than straightforward Monte Carlo. We plan to free software that implements these methods.
  • Eric Lecuyer (Université de Montréal), Blanchette Mathieu & Waldispühl Jérôme : Developing a machine learning framework to dissect gene expression control in subcellular space
    • Our multidisciplinary team will develop and use an array of machine learning approaches to study a fundamental but poorly understood process in molecular biology, the subcellular localization of messenger RNAs, whereby the transcripts of different human genes are transported to various regions of the cell prior to translation. The project will entail the development of new learning approaches (learning from both RNA sequence and structure data, phylogenetically related training examples, batch active learning) combined with new biotechnologies (large-scale assays of both natural and synthetic RNA sequences) to yield mechanistic insights into the “localization code” and help understand its role in health and disease.
  • Sébastien Lemieux (Université de Montréal), Bengio Yoshua , Sauvageau Guy & Cohen Joseph Paul : Deep learning for precision medicine by joint analysis of gene expression profiles measured through RNA-Seq and microarrays
    • This project aims at developing domain adaptation techniques to enable the joint analysis of gene expression profiles datasets acquired using different technologies, such as RNA-Seq and microarrays. Doing so will leverage the large number of gene expression profiles publicly available, avoiding the typical problems and limitations caused by working with small datasets. More specifically, methods developed will be continuously applied to datasets available for Acute Myeloid Leukemia in which the team has extensive expertise.
  • Andrea Lodi (Polytechnique Montréal), Bengio Yoshua, Charlin Laurent, Frejinger Emma & Lacoste-Julien Simon : Machine Learning for (Discrete) Optimization
    • The interaction between Machine Learning and Mathematical Optimization is currently one of the most popular topics at the intersection of Computer Science and Applied Mathematics. While the role of Continuous Optimization within Machine Learning is well known, and, on the applied side, it is rather easy to name areas in which data-driven Optimization boosted by / paired with Machine Learning algorithms can have a game-changing impact, the relationship and the interaction between Machine Learning and Discrete Optimization is largely unexplored. This project concerns one aspect of it, namely the use of modern Machine Learning techniques within / for Discrete Optimization.
  • Alejandro Murua (Université de Montréal), Quintana Fernando & Quinlan José : Gibbs-repulsion and determinantal processes for statistical learning
    • Non-parametric Bayesian models are very popular for density estimation and clustering. However, they have a tendency to use too many mixture components due to their use of independent parameter priors. Repulsion processes priors such as determinantal processes, solve this issue by putting higher mass on parameter configurations for which the mixture components are well separated. We propose the use of Gibbs-like repulsion processes which are locally determinantal, or adaptive determinantal processes as priors for modeling density estimation, clustering, and temporal and/or spatial data.
  • Marcelo Vinhal Nepomuceno (HEC Montréal), Charlin Laurent, Dantas Danilo C., & Cenesizoglu Tolga : Using machine learning to uncover how marketer-generated post content is associated with user-generated content and revenue
    • This projects proposes how machine learning can be used to improve a company’s communication with its customers in order to increase sales. To that end, we will identify how broadcaster-generated content is associated with user-generated content and revenue measures. In addition, we intend to automate the identification of post content, and to propose personalized recurrent neural networks to identify the writing styles of brands and companies and automate the creation of online content.
  • Dang Khoa Nguyen (Université de Montréal), Sawan Mohamad, Lesage Frédéric, Zerouali Younes & Sirpal Parikshat : Real-time detection and prediction of epileptic seizures using deep learning on sparse wavelet representations
    • Epilepsy is a chronic neurological condition in which about 20% of patients do not benefit from any form of treatment. In order to diminish the impact of recurring seizures on their lives, we propose to exploit the potential of artificial intelligence techniques for predicting the occruence of seizures and detecting their early onset, such as to issue warnings to patients. The aim of this project is thus to develop an efficient algorithm based on deep neural networks for performing real-time detection and prediction of seizures. This work will pave the way for the development of intelligent implantable sensors coupled with alert systems and on-site treatment delivery.
  • Jian-Yun Nie (Université de Montréal), Langlais Philippe, Tang Jian & Tapp Alain : Knowledge-based inference for question answering and information retrieval
    • Question answering (QA) is a typical NLP/AI problem with wide applications. A typical approach first retrieves relevant text passages and then determines the answer from them. These steps are usually performed separately, undermining the quality of the answers. In this project, we aim at developing new methods for QA in which the two steps can benefit from each other. On one hand, inference based on a knowledge graph will be used to enhance the passage retrieval step; on the other hand, the retrieved passages will be incorporated into the second step to help infer the answer. We expect the methods to have a higher capability of determining the right answer.
  • Jean-François Plante (HEC Montréal), Brown Patrick, Duschesne Thierry & Reid Nancy : Statistical modelling with distributed systems
    • Statistical inference requires a large toolbox of models and algorithms that can accommodate different structures in the data. Modern datasets are often stored on distributed systems where the data are scattered across a number of nodes with limited bandwidth between them. As a consequence, many complex statistical models cannot be computed natively on those clusters. In this project, we will advance statistical modeling contributions to data science by creating solutions that are ideally suited for analysis on distributed systems.
  • Doina Precup (McGill University), Bengio Yoshua & Pineau Joelle : Learning independently controllable features with application to robotics
    • Learning good representations is key for intelligent systems. One intuition is that good features will disentangle distinct factors that explain variability in the data, thereby leading to the potential development of causal reasoning models. We propose to tackle this fundamental problem using deep learning and reinforcement learning. Specifically, a system will be trained to discover simultaneously features that can be controlled independently, as well as the policies that control them. We will validate the proposed methods in simulations, as well as by using a robotic wheelchair platform developed at McGill University .
  • Marie-Ève Rancourt (HEC Montréal), Laporte Gilbert, Aloise Daniel, Cervone Guido, Silvestri Selene, Lang Stefan & Bélanger Valérie : Analytics and optimization in a digital humanitarian context
    • When responding to humanitarian crises, the lack of information increases the overall uncertainty. This hampers relief efforts efficiency and can amplify the damages. In this context, technological advances such as satellite imaging and social networks can support data gathering and processing to improve situational awareness. For example, volunteer technical communities leverage ingenious crowdsourcing solutions to make sense of a vast volume of data to virtually support relief efforts in real time. This research project builds on such digital humanitarianism initiatives through the development of innovative tools that allow evidence-based decision making. The aim is to test the proposed methodological framework to show how data analytics can be combined with optimization to process multiple sources of data, and thus provide timely and reliable solutions. To this end, a multidisciplinary team will work on two different applications: a sudden-onset disaster and a slow-onset crisis.
  • Louis-Martin Rousseau (Polytechnique Montréal), Adulyasak Yossiri, Charlin Laurent, Dorion Christian, Jeanneret Alexandre & Roberge David : Learning representations of uncertainty for decision making processes
    • Decision support and optimization tools are playing an increasingly important role in today’s economy. The vast majority of such systems, however, assume the data is either deterministic or follows a certain form of theoretical probability functions. We aim to develop data driven representations of uncertainty, based on modern machine learning architectures such as probabilistic deep neural networks, to capture complex and nonlinear interactions. Such representations are then used in stochastic optimization and decision processes in the fields of cancer treatment, supply chain and finance.
  • Nicolas Saunier (Polytechnique Montréal), Goulet James, Morency Catherine, Patterson Zachary  & Trépanier Martin : Fundamental Challenges for Big Data Fusion and Strategic Transportation Planning
    • As more and more transportation data becomes continuously available, transportation engineers and planners are ill-equipped to make use of it in a systematic and integrated way. This project aims to develop new machine learning methods to combine transportation data streams of various nature, spatial and temporal definitions and pertaining to different populations. The resulting model will provide a more complete picture of the travel demand for all modes and help better evaluate transportation plans. This project will rely on several large transportation datasets.
  • Yvon Savaria (Polytechnique Montréal), David Jean-Pierre, Cohen-Adad Julien & Bengio Yoshua : Optimised Hardware-Architecture Synthesis for Deep Learning
    • Deep learning requires considerable computing power. Computing power can be improved significantly by designing application specific computing engines dedicated to deep learning. The proposed project consists of designing and implementing a High Level Synthesis tool that will generate an RTL design from the code of an algorithm. This tool will optimize the architecture, the number of computing units, the length and representation of the numbers and  the important parameters of the various memories generated.
  • Mohamad Sawan (Polytechnique Montréal), Savaria Yvon & Bengio Yoshua : Equilibrium Propagation Framework: Analog Implementation for Improved Performances (Equipe)
    • The main aim of this project is to implement the Equilibrium Propagation (EP) algorithm in analog circuits, rather than digital building blocks, to take advantage of their higher computation speed and power efficiency. EP involves minimization of an energy function, which requires a long relaxation phase that is costly (in terms of time) to simulate on digital hardware. But it can be accelerated through analog circuit implementation. Two main implementation phases in this project are: (1) Quick prototyping and proof of concep using an FPAA platform (RASP 3.0), and (2) High performance custom System-on-Chip (SoC) implementation using a standard CMOS process e.g. 65nm to optimize the area, speed, and power consumption.
  • François Soumis (Polytechnique Montréal), Desrosiers Jacques, Desaulniers Guy, El Hallaoui Issmail, Lacoste-Julien Simon, Omer Jérémy & Mohammed Saddoune : Combiner l’apprentissage automatique et la recherche opérationnelle pour traiter plus rapidement les grands problèmes d’horaires d’équipages aériens
    • Nous travaux récents portent sur le développement d’’algorithmes d’’optimisation exacts qui profitent de l’’information a priori sur les solutions attendues pour réduire le nombre de variables et de contraintes à traiter simultanément. L’objectif est de développer un système d’apprentissage machine pour obtenir l’’information permettant d’accélérer le plus possible ces algorithmes d’’optimisation, pour traiter de plus grands problèmes d’’horaires d’’équipages aériens. Ce projet produira en plus des avancements en R. O. des avancements en apprentissage sous contraintes et par renforcement.
  • An Tang (Université de Montréal), Pal Christopher, Kadoury Samuel, Bengio Yoshua, Turcotte Simon, Nguyen Bich & Anne-Marie Mes-Masson : Predictive model of colorectal cancer liver metastases response to chemotherapy
    • Colon cancer is the 2nd leading cause of mortality in Canada. In patients with colorectal liver metastases, response to chemotherapy is the main determinant of patient survival. Our multidisciplinary team will develop models based to predict response to chemotherapy and patient prognosis using the most recent innovations in deep learning architectures. We will train our model on data from an institutional biobank and validate our model on independent provincial imaging and medico-administrative databases.
  • Pierre Thibault (Université de Montréal), Lemieux Sébastien, Bengio Yoshua & Perreault Claude : Matching MHC I-associated peptide spectra to sequencing reads using deep neural networks
    • Identification of MHC I-associated peptides (MAPs) unique to a patient or tumor is key step in developing efficacious cancer immunotherapy. This project aims at developing a novel approach for exploiting Deep Neural Networks (DNN) for the identification of MAPS based on a combination of next-generation sequencing (RNA-Seq) and tandem mass spectrometry (MS/ MS). The proposed developments will take advantage of a unique dataset of approximately 60,000 (MS/MS – sequence) pairs assembled by our team. The project will also bring together researchers from broad horizons: mass spectrometry, bioinformatics, machine learning and cancer immunology