Stages postdoctoraux

Programme de stages postdoctoraux IVADO

Engagement d’IVADO pour l’équité, la diversité et l’inclusion et note aux candidat(e)s :
Afin que l’avancement des connaissances et des opportunités dans le domaine de la science des données bénéficie équitablement à tous les membres de la société, IVADO promeut des principes d’équité, de diversité et d’inclusion à travers l’ensemble de ses programmes. IVADO s’engage à offrir un processus de recrutement et un milieu de recherche qui soient inclusifs, non discriminatoires, ouverts et transparents.

En bref

Description

FAQ

Soumission

Résultats - concours hiver 2018

Résultats - concours été 2018

  • Domaine d’étudeLe programme de financement IVADO pour stages postdoctoraux soutient la recherche concernant les enjeux soulevés dans la demande de subvention Apogée : la science des données au sens large, incluant des recherches méthodologiques en science des données (apprentissage automatique, recherche opérationnelle, statistiques) et leurs applications dans plusieurs domaines dont les domaines prioritaires d’IVADO (la santé, le transport, la logistique, l’énergie, le commerce et la finance) ou tout autre domaine d’application (sociologie, physique, linguistique, génie, etc.).
  • Montant en salaire (et non en bourse) et durée
    • 70 000 $ par année pour les financements réguliers (incluant les avantages sociaux) pour un maximum de deux ans entièrement financés par IVADO.
    • 70 000 $ par année, financés à moitié par IVADO (35 000 $) et à moitié par un partenaire (35 000 $), pour les financements en partenariat (incluant les avantages sociaux) pour un maximum de deux ans.
    • 90 000 $ par année pour les financements Fellow (incluant les avantages sociaux) pour un maximum de trois ans entièrement financés par IVADO. 10 000 $ peuvent être ajoutés, la première année, sur demande pour des frais de relocalisation et 15 000 $ par année pour financer des activités de recherche.
    • Pour les canditat(e)s provenant des pays bénéficiaires de l’aide pour le développement du Canada, les frais de relocalisation, pour débuter le postdoctorat à Montréal, pourront être remboursés sur demande.
  • Date limite de présentation de la demande : 18 novembre 2018
  • Date de notification prévue : début janvier 2019
  • Critères : voir l’onglet description
  • Soumission : voir l’onglet soumission
  • Renseignements : programmes-excellence@ivado.ca

Objectifs du programme

  • Former les futur(e)s chercheur(e)s et professeur(e)s et plus généralement, les futur(e)s acteur(-trice)s dans le secteur de la science des données, principalement dans les domaines d’excellence des membres IVADO : recherche opérationnelle, apprentissage machine et sciences de la décision;
  • Promouvoir la mobilité, le recrutement et la rétention des jeunes chercheur(e)s;
  • Encourager le développement de la recherche de pointe collaborative et appliquée.

Candidats admissibles

  • Pour le (la) candidat(e) au postdoctorat :
    1. Avoir reçu son premier doctorat depuis moins de cinq ans au moment de la demande ou prévoir de l’obtenir à la date de l’annonce des résultats (décembre 2018). IVADO sera flexible pour les candidat(e)s justifiant les interruptions de carrière et les circonstances particulières, nous invitons à l’indiquer dans votre demande (p.ex. pour cause de maternité ou de maladie).
    2. Prévoir s’inscrire à un programme à HEC Montréal, Polytechnique Montréal, l’Université de Montréal, l’Université McGill ou l’Université de l’Alberta.
    3. Ne pas avoir eu son affiliation principale à Montréal depuis 1 an. Cette mesure vise à promouvoir l’attraction des chercheur(e)s internationaux talentueux.
  • Pour le (la) professeur(e) présentant la demande (superviseur[e]) :
    1. Être un (une) professeur(e) de l’une des institutions suivantes : HEC Montréal, Polytechnique Montréal ou l’Université de Montréal;
    2. Les professeur(e)s de l’Université de l’Alberta et de l’Université McGill peuvent être superviseur(e)s à condition qu’ils (elles) soient également membres de l’un des groupes de recherche d’IVADO (MILA, CIRRELT, GERAD, CERC Science des données, CRM, Tech3Lab);
    3. Ne pas avoir été le (la) directeur(-trice) ou le (la) codirecteur(-trice) du (de la) candidat(e) en tant que doctorant(e);
    4. Ne pas présenter plus d’une demande de candidature pour ce concours.

Début de la bourse

Date de début du financement : entre Janvier et mars 2019

Montant et conditions de versement

Les fonds (salaires et remboursements de réinstallation potentiels) seront transférés au Bureau de la recherche de l’université du (de la) superviseur(e), et l’université paiera le (la) chercheur(le) postdoctoral(e) en fonction de ses propres règles. Pour les projets nécessitant une approbation éthique, les fonds ne seront pas débloqués tant que l’approbation ne sera pas obtenue. Certains projets, notamment en partenariat, pourraient avoir besoin d’accords spécifiques, par exemple en ce qui concerne la propriété intellectuelle.

Le financement pourra être réduit, retenu, retardé ou annulé lors de situations particulières qui seront explicitées dans la lettre d’octroi.

Processus du concours

Évaluation et critères

Les propositions seront passées en revue pour vérifier les exigences du programme (demande incomplète, trop longue, superviseur(e) ou candidat(e) non admissible, etc.) et l’adéquation du projet de recherche soumis avec la science des données. Seules les demandes répondant aux exigences seront transmises au comité d’évaluation.

Le comité d’évaluation paritaire sera composé de professeur(e)s de l’une des institutions suivantes : HEC Montréal, Polytechnique Montréal, l’Université de Montréal ou McGill, bien au fait des domaines d’excellence d’IVADO, et qui ne présenteront aucun(e) candidat(e). En raison de la petite taille de la communauté dans certains domaines, il pourrait être difficile de trouver des examinateur(-trice)s pertinent(e)s ne présentant aucun(e) candidat(e). Face à une telle situation, un(e) évaluateur(-trice) pourrait être appelé(e) à évaluer un(e) candidat(e) bien qu’il (elle) participe lui (elle)-même au concours; un(e) examinateur(-trice) externe pourrait également être appelé(e). Le comité veillera par tous les moyens à ce que l’évaluateur(-trice) n’influe pas le classement de sa propre application.

Le comité d’évaluation procédera ensuite au classement des candidat(e)s selon l’excellence de leur dossier, mais aussi selon l’adéquation du projet dans le cadre général d’IVADO, qui est de promouvoir la collaboration multidisciplinaire et la diversité dans la science des données. En ce qui regarde l’excellence du dossier du (de la) candidat(e), le comité s’intéressera plus spécifiquement :

  • à ses contributions à la recherche  (impact scientifique, qualité de la recherche, productivité, financements obtenus)
  • à la qualité de sa thèse de doctorat et à son excellence académique,
  • à l’étendue et à la portée de son expérience (expériences pluridisciplinaires, expérience professionnelle, activités parascolaires ou extra-universitaires, collaborations, services à la communauté scientifique, contribution à la société, etc.)
  • et à l’adéquation de l’expérience avec le projet proposé.

L’ordonnancement des candidat(e)s pour le financement en partenariat sera basé en plus sur le potentiel de recherche appliquée du projet.

Le comité d’évaluation procédera au classement des candidat(e)s demandant un financement postdoctoral régulier et en partenariat. Pour les candidat(e)s demandant un financement postdoctoral régulier, des financements Fellow éventuels pourront être attribués en séance par le comité d’évaluation pour les candidatures exceptionnelles.

Finalisation du dossier et engagements des chercheurs postdoctoraux

Le (la) chercheur(e) postdoctoral(e) s’engage notamment à :

  • Être physiquement présent(e) à l’université de son (sa) superviseur(e), ou de partager son temps entre son université d’attache et l’entreprise/organisation partenaire en cas de financement en partenariat;
  • Participer à la communauté et aux activités IVADO, incluant par exemple :
    • les présentations sur leur propre recherche;
    • les activités de formation ou de diffusion des connaissances;
    • les consultations;
    • le soutien lors de différentes activités qui font normalement partie d’une carrière de recherche (mentorat, notamment dans le cadre du projet SEUR, participation à des évaluations, coorganisation d’événements, etc.);
  • Reconnaître qu’il (elle) fait partie d’une communauté universitaire, à laquelle il (elle) contribuera;
  • Se soumettre à la politique des trois organisations sur le libre accès aux publications. Il (elle) est encouragé(e) à publier ses productions de recherche (publications, enregistrement oral des présentations, code source, bases de données, etc.) dans le respect des règles de propriété intellectuelle applicables dans son cas particulier;
  • Reconnaître le soutien d’IVADO et d’Apogée / CFREF ou du FRQ dans la diffusion des résultats de recherche et plus généralement dans les diverses activités auxquelles le (la) chercheur(e) postdoctoral(e) participera.

Le (la) superviseur(e) s’engage notamment à :

  • Fournir un environnement de travail qui convient à l’achèvement du projet;
  • Superviser son (sa) chercheur(e) postdoctoral(e).

FAQ

  • Les lettres de recommandation doivent-elles être incluses dans le fichier unique de candidature ?
    • Non, elles doivent être envoyées par leur auteur.
  • J’ai fait mon doctorat dans un pays dans lequel il n’y avait pas de relevé de notes pour le doctorat. Que dois-je faire ?
    • Vous devez le mentionner dans votre dossier de candidature et fournir vos relevés de Maîtrise/Master à la place.

D’autres questions? Veuillez les soumettre à programmes-excellence@ivado.ca

Le dossier comprendra :

  •      ce formulaire, rempli;
  •      le C.V. de l’étudiant(e), dans un format libre;
  •      les relevés de notes officiels du doctorat (préciser l’échelle de notation pour les universités non canadiennes);
  •      la description du projet (une page maximum);
  •      un maximum de trois lettres de recommandation (incluant une du (de la) superviseur(e) et une du (de la) directeur[-rice] de thèse) envoyées par eux (elles) à programmes-excellence@ivado.ca;
  •      une lettre de confirmation du partenaire (si le (la) candidat(e) et le(la) superviseur(e) veulent explicitement un financement postdoctoral en partenariat).

Le dossier de candidature complet doit être envoyé à programmes-excellence@ivado.ca et en copie conforme (CC) au (à la) superviseur(e), sous la forme d’un seul fichier pdf.

  • Behrouz Babaki (Polytechnique Montréal, Gilles Pesant)
    • To turn the ever increasing amounts of data into social and economic value, two tasks need to be performed: 1) extracting knowledge from the data, and 2) incorporating this knowledge in the operations that drive the society. The machine learning community addresses the first task by extracting the knowledge from the data and capturing it into ‘learned models’. The second task is studied by the operations research community under the label of ‘optimization’. However, these techniques have been developed almost independently. This makes it less straightforward to integrate them and turn the knowledge obtained from a learned model into actionable decisions. In this project, we exploit the fundamental similarities between the two tasks to develop an integrated system that performs both tasks together. We apply our system to problems in business and finance and demonstrate how this approach can help players in these sectors to use their data for improving their operations.
  • Maxime Laborde (McGill, Adam Oberman)
    • This research is focused on using mathematical tools to accelerate the training time of Deep Neural Networks (DNN)s. DNNs are a powerful tool in Artificial Intelligence, behind applications in machine translation, image recognition, speech recognition and other areas. However training the DNNs requires huge computational resources, which is costly both financially, and in the human effort required to implement them. This research will use advanced mathematical tools to improve the time required to train DNNs.
  • Tien Mai (Université de Montréal, Teodor Gabriel Crainic)
    • This project deals with the planning of intermodal rail transportation, integrating methodologies from operations research and machine learning in a new and innovative way. Intermodal container freight transportation is the backbone of international trade and supports a large part of Canadian and North-American imports and exports. Canada has one of the largest rail networks in the world and Canadian railway companies are both network and terminal operators. They face many large-scale optimization problems that are complex because of their sheer size and the uncertainty that affects planning and operations on a continuous basis. The project focuses on a tactical network load and block planning problem that involves decisions related to blocking and railcar fleet management. Assuming that the train schedule is given, the problem entails three consolidation processes: assignment of containers to railcars, of railcars to blocks and of blocks to trains. The project will be dedicated to designing a service network design model and associated solution method that allows to solve realistic, large scale, instances.
  • Abbas Mehrabian (McGill, Luc Devroye)
    • When designing a machine learning algorithm, it is crucial for the designer to understand the input data to which this algorithm will be applied. It is well known that real-world data for any task has a lot of structure, exploiting which allows for faster learning and more accurate prediction. However, understanding this structure is a highly nontrivial task, given the high dimension of the data. In this project we propose to develop a mathematical framework for learning the structure hidden in the data, via the lens of probability theory. Assuming the data is generated by some stochastic process, we would like to infer its distributional properties. Then a natural question is, which distributions are harder to learn, and which ones are easier. The aim of this project is to answer this question from statistical and computational perspectives, at least for a variety of commonly used classes of distributions, such as mixture models and graphical models.
  • Patrick Munroe (Polytechnique Montréal, François Soumis)
    • Gestion en temps réel du cargo aérien. Le projet à moyen terme est le développement d’un système de gestion du cargo dans les compagnies aériennes en commençant avec Air Canada. Ce système traitera la planification stratégique, tactique et l’opération en temps-réel. Le niveau stratégique évalue des scénarios à long terme sur l’organisation du réseau, les marchés à développer, les alliances à conclure. Le niveau tactique optimise le choix des itinéraires entre chaque paire de villes pour une semaine type d’une saison. Durant l’opération, les vendeurs pourront obtenir en ligne le meilleur itinéraire pour acheminer une nouvelle commande et le prix de revient. À chaque niveau de décision, il faut estimer la demande pour l’horizon considéré et optimiser l’acheminement de cette demande dans le réseau de transport comprenant des avions tout cargo, l’espace disponible dans les soutes des vols passagers, des sous-contrats avec d’autres transporteurs aériens et routiers. La recherche portera sur le développement de nouvelles méthodes d’estimation de la demande et d’optimisation de l’acheminement dans un grand réseau.
  • Maria Isabel Restrepo Ruiz (Polytechnique Montréal, Nadia Lahrichi)
    • The main objective in using optimization approaches for demand and supply management in home healthcare is to match supply and demand by influencing patients/caregivers choices for service time slots/working shifts. Our aim with this project is to develop a decision support tool to deal with approaches for demand and supply management in home healthcare. Specifically, we will implement stochastic models to forecast future demands and to predict caregivers’ absenteeism. Then, we will design and develop choice models to consider patient and caregiver choice behavior. These models will predict the probability of choosing a particular alternative from an offered set (e.g. visit time slots, working shifts) given historical choice data about an individual or a segment of similar individuals. These models will be embedded into an optimization approach that will compute a time slotting/scheduling plan or a pricing strategy to optimally balance the allocation of cost-effective schedules to caregivers and the improvement of service quality.
  • Anne-Lise Saive (Université de Montréal, Karim Jerbi)
    • Every day, we experience thousands of situations, but we only remember few of them. Episodic memory is the only memory system that allows people to consciously re-experience past experiences and it is the most sensitive to age and neurodegenerative diseases. It is thus critical to better understand how to enhance learning and memory in both healthy and clinical populations. Emotions are known to robustly strengthen the formation of long-term memories. Characterizing the influence of positive emotions (joy, happiness) on memory could be pivotal in improving memory therapies, yet the underlying brain mechanisms are still surprisingly misunderstood. In this project, we will use a fully data-driven approach to identify the key neuronal processes strengthened by positive emotions that distinguish events we will durably remember from events we will forget. We will combine for the first time high spatial and temporal resolution brain imaging techniques and state-of-the-art machine-learning algorithms. This will be achieved by assessing the ability of multidimensional (across space, time and frequency) arrays of brain data to predict future memory accuracy.
  • Rabih Salhab (HEC Montréal, Georges Zaccour)
    • Ride-sharing services such as Uber, Lyft or Didi Chuxing match a group of drivers providing rides with customers through an online ride-sharing platform. This business model faces a number of fundamental challenges. Indeed, the drivers considered as independent contractors choose the area they wish to serve, if they accept or reject rides, and when they start and stop working. With no direct control over the drivers, the ride-sharing platform can only use incentives and select the information it provides to drivers and customers in order to improve the quality of service and balance supply and demand. This project aims to develop a model that anticipates how the drivers respond to provided information, which is a combination of request statistics, prices at various locations and times, and estimation of the state of the road network. Moreover, it intends to generate location and time-dependent pricing schemes and optimal information filters in order to optimize the efficiency of the system. For example, the filters control the amount of information to release to drivers about the requests in order to balance the supply and demand and avoid the drivers from deserting some areas.
  • Kristen Schell (Polytechnique Montréal, Miguel Anjos)
    • Hydro-Québec is geographically well positioned to make significant profits in neighboring electricity markets. Facing political mandates to retire coal and nuclear power plants, the markets of Ontario, New York and New England are under increasing stress to provide stable, baseload electricity production. We will utilize the vast historical data from these markets to model their future evolution. Using the insights obtained from this analysis, we will be able to determine optimal strategies for Hydro-Québec to maximize its profits through targeted investment decisions in market interconnections. The results will be generalizable to other provincial utilities in Canada and their participation in the relevant electricity markets.
  • Jean-François Spinella (Université de Montréal, Guy Sauvageau)
    • Acute myeloid leukemia (AML) is the most common form of leukemia in adults. Despite advances in supportive care to treat therapy-related complications, the majority of AML patients will not exceed the two-year survival mark because of relapse. This dismal outcome reflects the sub-optimal treatment orientation of poorly understood subtypes of AML. To improve the treatment and outcome of patients, Dr. Guy Sauvageau and colleagues initiated in 2009 the Leucegene project which has become an internationally acknowledged leader in genetic and biological characterization of AML. Exploiting the most innovative technologies, this program already allowed the sequencing of 452 primary human AML specimens. While several types of genomic alterations have been explored in AML, some of them, such as modifications to the chromosome structure, remain elusive despite their known importance in cancer. We are convinced that this is due to unsuitable analysis and we propose here an innovative machine learning approach to efficiently identify these modifications. Tests will be carried out on our sequenced AML specimens. Ultimately, the method will be released to help the scientific community to exploit its cancer data. From a biomedical point of view, it will allow for better definition of AML subgroups, as well as an increase in the chances of identifying new markers for this disease. With the goal to accelerate the transfer of new knowledge from the laboratory to the bedside, this project will help ensure the correct classification and treatment of AML.
  • Yu Zhang (Université de Montréal, Pierre Bellec)
    • To understand brain mechanism of cognitive functions is the ultimate goal of neuroscience studies, which also provides fundamental guidance for developing new techniques in artificial intelligence. With accumulated evidence in animals and humans, functional dynamics is suggested to be the non-stationary nature of cognitive process. In this project, we aim to apply deep learning models to characterize the spatial and temporal dynamics of BOLD signals at rest and during cognitive tasks. To account for the temporal dependence of MRI signals, a convolutional recurrent neural network is first used to characterize the spatial and temporal dynamics of resting-state data, and then to map the dynamic somatotopic maps during movement of tongue, hand and foot. The model is further adjusted for classification of functional dynamics among multiple task conditions. The derived characteristic functional dynamics, including sequential temporal response functions and corresponding activation patterns, reveals the dynamic process of human cognitive function and provides essential guidance for brain simulation. Furthermore, our proposed method could also be used in clinic applications, for instance searching for temporal and spatial biomarkers for Alzheimer’s disease and evaluating the treatment effects of precision medicine.
  • Jonathan Binas (Université de Montréal, Yoshua Bengio)
    • Recent machine learning approaches have led to impressive demonstrations of machines solving a great variety of difficult tasks, which previously were thought to be restricted to humans. Applied to areas such as health care, environmental challenges, optimization of transport and logistics, or industrial processes, these advances will lead to improved living conditions and the creation of value. While being loosely inspired by biological neural systems, artificial neural networks starkly differ from their biological counterparts in almost every respect. In particular,
      brains can learn from very few examples, infer causal relationships, and seamlessly transfer skills to new tasks, whereas current machine learning models require enormous amounts of data to just master a single task. To overcome some of these limitations, we introduce new, brain-inspired models for learning and memory, which will allow for meaningful information to be extracted from data more efficiently. The resulting systems will lead to improved, more powerful machine learning systems, which can be applied in numerous contexts, including medical applications, automation, robotics, or forecasting.
  • Marco Bonizzato (Université de Montréal, Marina Martinez)
    • A quarter million people every year are affected by spinal cord injury (SCI), which causes paraplegia. When the lesion is incomplete some recovery can occur. Spinal cord stimulation can be applied to help people with SCI to regain control of the paralyzed legs. In the last year Prof. Martinez and I demonstrated a new neuroprosthetic concept whereby cortical stimulation is applied to improve walking. This novel strategy empowers the brain’s own residual networks and increases voluntary control of leg movement with long lasting beneficial effects for recovery. « Fire together, wire together » is the established rule for neural repair. Here we propose to combine for the first time brain and spinal stimulation into an unique combined neuroprosthesis. This approach is compelling, but complicated by the overwhelming amount of stimulation parameters that needs to be characterized. We propose to solve this problem with machine learning. The first ever intelligent neuroprosthesis will monitor changes in muscular activity to explore and learn an optimal set of stimulation parameters. Our results can be rapidly translated to clinical tests.
  • Elie Bou Assi (Université de Montréal, Dang K. Nguyen)
    • Epilepsy is a chronic neurological condition that affects as many as 1 in every 100 Canadians. While first line of treatment consists of long-term drug therapy more than a third of patients suffer from seizures that are resistant to antiepileptic drugs. Due to their unpredictable nature, uncontrolled seizures represent a major personal handicap and source of worriment for patients. In addition, persistent seizures constitute a considerable public health burden due to high use of health care resources, high number of disability days or unemployment, and low annual income. Some of the difficulties and challenges faced by drug-refractory patients can be overpassed by implementing algorithms able to anticipate seizures. With accurate seizure forecasting, one could ameliorate refractory epilepsy management improving social integration, productivity and quality of life. Our main objective is the development of a real-time seizure prediction system, based on deep learning, intended to warn patients or caretakers about an incoming seizure and recommend advisory measures.
  • Quentin Cappart (Polytechnique Montréal, Louis-Martin Rousseau)
    • L’optimisation combinatoire occupe une place prépondérante dans notre société actuelle. Que ce soit la logistique, le transport ou la gestion financière, tous ses domaines se retrouvent confrontés à des problèmes pour lesquels on recherche la meilleure solution. Cependant, un grand nombre de problèmes très complexes reste encore hors de portée des méthodes d’optimisation actuelles. C’est pourquoi, l’amélioration de ces techniques est un sujet crucial. Parmi ces dernières, les diagrammes de décisions semblent avoir un avenir prometteur. Un diagramme de décision est une structure qui permet de représenter de manière compacte un problème tout en préservant ses caractéristiques. Cependant, leur efficacité est extrêmement dépendante de l’ordre des variables utilisé pour leur construction. L’objectif de ce projet est d’utiliser les méthodes récentes d’apprentissage automatique pour ordonner les variables lors de la construction d’un diagramme de décision. Les contributions de ce projet permettront la résolution de problèmes combinatoires plus complexes, et plus larges que ce que peuvent faire les méthodes de l’état de l’art. Nous nous consacrerons principalement aux problèmes réels liés au transport et à la logistique. Ce projet sera effectué en partenariat avec l’entreprise Element-AI.
  • Jasmin Coulombe-Huntington (Université de Montréal, Micheal Tyers)
    • Drug combinations can simultaneously target redundant biological pathways and thus offer unique advantages for disease treatment. By growing human cancer cells each with a specific gene deletion in the presence of a drug, we identified gene deletions which make cells more sensitive or more resistant to the growth-inhibition effects of >230 different drugs. In this proposal, I outline a plan to develop software tools to exploit this resource in order to precisely characterize drug mechanisms and to predict useful drug combinations. Tumor growth relies on overactive biomass and energy production, and I found that close to 80% of the drugs we screened altered the sensitivity of cells to the deletion of metabolic genes. I will use a genome-scale mathematical model of cell metabolism to attribute these effects to the lowered activity of other metabolic genes, those whose activities we predict are directly affected by the drug. After modelling the effects of each drug on cell metabolism, we will simulate the effects of drug combinations to identify pairs which effectively block the generation of small molecules important to tumor growth.available data on the effectiveness of drug combinations, I will train a machine learning algorithm to use similar gene deletion data as well as drug molecular similarities to predict useful drug combinations for the treatment of cancer and potentially other diseases. I will also attempt to predict the direct molecular targets of each drug by modelling molecular signalling in cells, leveraging known signalling pathways, molecular interaction networks and pairs of gene deletions sensitive or resistant to similar sets of drugs.
  • Pouria Dasmeh (Université de Montréal, Adrian Serohijos)
    • The rise of antibiotic resistance has put antimicrobials, once believed to be miracles of modern medicine, into jeopardy1. The current death toll of AMR is ~800,000 per year (i.e., ~100 per hour) and is expected to rise to ~ 16 million in 2050. In Canada alone, the financial burden of antibiotic resistance is ~ $200 million annually. A key knowledge in our battle against antibiotic resistance is to predict the growth rate of bacteria at different concentrations of antibiotics. Recently, the response of bacterial strains to antibiotics were measured for all possible mutations in important enzymes that confer resistance to beta-lactam antibiotics (e.g., penicillins, ampicillins, etc.). In this project, I will employ the power of machine learning to develop predictive models of resistance at different antibiotic dosages from the available large-scale datasets. This approach would have immediate impacts on the design of antibiotic dosages that prevent or delay the onset of resistance. In this integration of machine learning with biochemistry and molecular medicine, we will seek the potentials of data science to aid decision-making in medicine.
  • Benoit Delcroix (Polytechnique Montréal, Michel Bernier)
    • Un défi majeur dans le secteur du bâtiment est l’’absence de systèmes continus de suivi de la performance et d’évaluation des écarts de performance entre la situation observée et celle désirée. L’opération non-optimale des systèmes de Chauffage, Ventilation et Conditionnement d’Air (CVCA) entraîne des pertes énergétiques et de confort des occupants. Le secteur du bâtiment représente environ un tiers de la consommation énergétique au Québec et au Canada. Ainsi, des mesures d’’efficacité dans ce secteur produisent des impacts positifs majeurs. L’idée de ce projet est d’utiliser des méthodes d’apprentissage profond pour exploiter les larges jeux de données générés par les systèmes CVCA. Le but final est d’automatiser la détection et le diagnostic des anomalies, et d’optimiser l’opération des équipements. Les bénéfices incluent une gestion améliorée de l’énergie et une meilleure prise en compte du confort des occupants. Au terme de ce projet, des outils de détection / diagnostic / contrôle basés sur l’apprentissage profond seront développés et testés à des fins d’implantation dans des bâtiments réels.
  • Golnoosh Farnadi (Polytechnique Montréal, Michel Gendreau)
    • The increasing use of algorithmic decision-making in domains that affect people’s lives, has raised concerns about possible biases and discrimination that such systems might introduce. Recent concerns on algorithmic discrimination have motivated the development of fairness-aware mechanisms in the machine learning (ML) community and the operations research (OR) community, independently. While in fairness-aware ML, the focus is usually on ensuring that the inference and predictions produced by a learned model are fair, the OR community has developed methods to ensure fairness in solutions of an optimization problem. In this project, I plan to build on the complementary strengths of fairness methods in ML and OR to address these shortcomings in a fair data-driven decision-making system. I will apply this work to real-world problems in the areas of personalized education, employment hiring (business), social well-being (health), and network design (transportation). The advantage of my proposed system compared to the existing works is that it: 1) incorporates domain knowledge with data-driven probabilistic models, 2) detects and describes complex discriminative patterns, 3) returns a fair decision/policy, and 4) breaks negative/positive feedback loops.
  • Kuldeep Kumar (Université de Montréal, Sébastien Jacquemont)
    • Neurodevelopmental disorders (NDs) including intellectual disabilities (ID), severe learning disabilities, and autism spectrum disorder (ASD) represent a significant health-care burden. The genetic contribution to NDs ranges from 50 to 80%. However, the interpretation of genetic mutations in the neurodevelopmental clinic remains, in many cases, a rough approximation. Over 3,500 whole genome chromosomal microarrays (CMAs) are performed yearly at the CHU-SJ, and for more than 95% of the mutations reported to patients, the effects on cognitive traits and brain function are unknown. With the development of the clinical exome, the proportion of undocumented rare mutations will continue to increase. To fill the widening gap between genome technologies and clinical neurosciences, we propose a ground-breaking strategy to accurately model the effects of copy-number-variations (CNVs), genome-wide, on cognition, brain structure and function. Towards this goal, recent machine learning techniques related to manifold approximation and deep learning will be investigated to learn a latent representation for multi-subject data that considers multi-modal information. In a large-scale analysis with over 20,000 individuals, multi-variate copy-number-variation (CNV) association analysis will assess the contribution of structure/function MRI modalities to rare genomic psychiatric risk factors. The deliverables of our project are models that can predict the effect of any rare mutation on cognition, brain structure and connectivity. This will allow clinicians to quantify the contribution of genetic variants to the neurodevelopmental symptoms of their patient.
  • Elizaveta Kuznetsova (Polytechnique Montréal, Miguel Anjos)
    • The cumulative solar and wind power capacity integrated mainly into low and medium voltage grids in Canada represents 9% of total available power capacity in 2015, and is expected to more than double by 2040. This reality will create not only opportunities for sustainable energy production, but also challenges for the system operator due to the uncertain power fluctuations from supporting multiple prosumers (customers who can alternatively behave as energy consumers or producers). This project addresses the question of how to involve prosumers in the energy management process for provision of ancillary services in the grid (e.g. voltage control) while mitigating unsuitable emerging effects. The idea is to consider a three-layer optimization problem related to different voltage levels (high, medium and low). Grid incentives will be optimized at the high voltage level, while lower levels will optimize the dispatch among grid prosumers to maximize their involvement. An Agent-Based Modelling framework will provide a backbone for this multi-level optimization, enable bi-directional information flows, and make it possible to handle the challenges of high data volume and complexity.
  • Tarek Lajnef (Université de Montréal, Karim Jerbi)
    • Our fast-paced performance-oriented modern societies have led to a serge in stress, anxiety and depression with severe impacts on sleep and the overall quality of life. Additionally, alterations in sleep also occur as a natural consequence of healthy aging and vary as a function of gender. Here we propose to apply state-of-art machine learning tools to brain data from large cohorts (14.000 individuals) in order to improve our understanding of the effect of aging and mood on patterns of brain activity assessed during sleep. We will apply multivariate machine learning techniques for classification and regression using a wide range of behavioural and electroencephalographic features in order to investigate the link between brain activity patterns observable during sleep and behavioural measures of anxiety/depression/stress. By exploring these links across the lifespan and as a function of gender, this research will pave the way for the development of novel diagnostic/therapeutic approaches.
  • Neda Navidi (Polytechnique Montréal, Nicolas Saunier)
    • Learning driving behavior from smartphone location and motion sensors Monitoring and tracking vehicles and driving behavior are of great interest to better assess safety and understand the relationship with potential factors related to the infrastructure, vehicles and users. This has been implemented in recent years by car insurance to better assess their customers’ risk of crash and offer usage-based premium. Driver monitoring and analysis or driver behavior profiling is the process of automatically collecting driving data (e.g., location, speed, acceleration) and predicting the crash risk. These systems are mainly based on Global Positioning System (GPS), which suffers from accuracy issues, e.g. in urban canyons, and is insufficient to detect normal and risky driving events like steering and braking to assess the driving behaviours. To address this problem, researchers have proposed the integration of GPS, Inertial Navigation System (INS) and motion sensors, and map-matching (MM) in a single hybrid system. INS is fused with GPS and used during signal outages to provide continuous positioning (dead reckoning). Map matching is the process of estimating a user’s position on a road segment, which provides more contextual information like road geometry and conditions, historical risk of the segment and other drivers’ behaviour. The objective of this work is to improve the understanding of driver behaviour and crash risk by integrating location and motion data, driving events and road attributes using different machine learning algorithms.The objective of this work is to improve the understanding of driver behaviour and crash risk by integrating location and motion data, driving events and road attributes. The specific objectives are the following: 1) to detect risky driving events, namely hard acceleration/braking, compliance to signalization (e.g. speed limits), sharp steering, tailgating, improper passing and weaving from location and motion data using machine learning (ML); 2) to apply map-matching algorithms to extract road-related attributes; 3) to cluster driver behaviour based on the time series of location and motion data, detected driving events and road-related attributes.
  • Nurit Oliker (Université de Montréal, Bernard Gendron)
    • We study the context of a transportation network manager who wants to take decisions on infrastructures, assets and resources to deploy in order to achieve its objectives. The network manager has to take into account that there are several classes of users, most of which pursue their own objectives within the rules stated by the manager, while others have objectives that are antagonist to those of the manager. Our goal is to develop methodology to help the transportation network manager. The application that motivates this research project is based on the transportation network design problem faced by a vehicle inspection agency who wants to inspect a maximum number of vehicles on a given territory under a limited budget. In such application, it is important to take into account the fact that some users will react to the installation of new vehicle inspection stations by diverting from their usual path to avoid inspection. Other applications of interest include the design of transportation networks that are resilient to major accidents and terrorist attacks. In this context, the network manager must anticipate potential threats posed by hostile users.
  • Camilo Ortiz Astorquiza (Université de Montréal, Emma Frejinger)
    • The railway industry represents one of the most important means of freight transportation. In Canada only more than 900,000 tons of goods are moved every day. where one of the major companies of the sector is Canadian National Railways (CN). An important component in their overall structure is the locomotive fleet management. The high cost of each locomotive and the large number of them required to satisfy train schedules makes the locomotive planning highly valuable. This in turn, represents an environmental and macroeconomic effect of great importance. Although several variants of locomotive planning problems have been studied before there is still a huge gap between the state-of-practice and the state-of-the-art. Thus, we will first study an optimization model that is tailored for CN’s requirements. Moreover, we will investigate on the development of specialized solution methods that incorporate machine learning with operations research techniques to obtain optimal solutions within reasonable time. This will provide a tool for the partner company to better evaluate scenarios in the locomotive planning and give value to the data while representing an important scientific contribution for the optimization community.
  • Musa Ozboyaci (Université de Montréal, Sebastian Pechmann)
    • Protein homeostasis describes the cells capability to keep its proteins in their correct shape and function through a complex regulatory system that integrates protein synthesis, folding and degradation. How cells maintain protein homeostasis is a fundamental phenomenon, an understanding of which has direct implications for prevention and treatment of severe human diseases such as Alzheimer’s and Parkinson’s. The protein quality control is regulated through specific enzymes called molecular chaperones that assist the (re)folding of proteins thus managing a complex and varied proteome efficiently. Although the specificity of interactions of these chaperones with their client proteins is known to be the key to the efficient allocation of protein quality control capacity, a significant yet unanswered question lies in rationalizing the principles of this specificity. This project aims to systematically define the principles of sequence specificity across eukaryotic chaperone network through a combination of molecular modelling and machine learning methods. To this end, the peptide sequences that confer chaperone specificity will be identified systematically using a robust docking procedure accelerated by a Random Forest model. To account for the conditional interdependencies of the energetic contributions of the peptide residues binding to the chaperone receptor and to capture them, probabilistic graphical models will be developed and deep learning methods will be applied to the large dataset obtained from docking simulations. This project, through the unique and rich dataset we will construct and the sophisticated analyses we will apply, will not only unravel the sequence specificity in protein homeostasis interactions during health and disease, but also provide the necessary guidelines for how it can be re-engineered for rational therapeutic intervention.
  • Maximilian Puelma Touzel (Université de Montréal, Guillaume Lajoie)
    • Recurrent neural nets are neuroscience-inspired AI algorithms that are revolutionizing the machine learning of complex sequences. They help power a variety of widely used applications such as Google Translate and Apple’’s Siri. But they are also big, complicated models, and learning them is a delicate process, up to now requiring much fine-tuning to avoid the parameter adjustments getting out of control. The human brain also faces this stability problem when it learns sequences, but it has a robust, working solution that we are only beginning to understand. Bringing together experts in neuroscience, applied math, and artificial intelligence, we will adapt sophisticated methods for measuring stability from the mathematics of dynamical systems. We will develop learning algorithms that use this information to efficiently guide the learning, and will employ them in a neuroscience study that compares artificial and brain solutions to learning complex task sequences. Our goal is to unify and extend our understanding of how natural and artificial recurrent neural nets learn complex sequences.
  • Raphael Harry Frederico Ribeiro Kramer (Polytechnique Montréal, Guy Desaulniers)
    • Facility location arises as an important field in combinatorial optimization with applications to logistics and data mining. In facility location problems (FLPs), one seeks to find the location of some supply points and to assign customers to those supply points so as to optimize a certain measure of performance. In data mining, several FLPs can be used with the purpose of modelling and solving clustering problems. The p-center problem (PCP) is an example of such type of problem, in which one seeks to find the location of p points (namely the centers) so as to minimize the maximum dissimilarity between any customer and its closest center. This problem is extremely difficult in practice. In a recent article co-authored by the candidate, the most classical variant of the PCP (namely the vertex PCP) is solved by an iterative algorithm for problems containing up to a million data points within reasonable time limits. This is more than 200x larger than previous algorithms. In this project we aim at extending some of the ideas used in that article to solve other classes of facility location problems for large datasets.
  • Joshua Stipancic (HEC Montréal, Aurélie Labbe)
    • Road traffic crashes are a serious concern. Typically, dangerous locations in the road network are identified based on historical crash data. However, using crashes is not ideal, as crash data bases contain error and omissions and crashes are not perfect predictors of safety. Our earlier work demonstrates how mobile sensor data, such as GPS travel data collected from regular drivers, can be used to substitute crash data in the safety management process within Quebec City. However, advanced statistical models must be developed to convert the collected sensor data into predicted crash counts at sites throughout the network. This project proposes three advancements to crash models developed in previous work. First, methods for imputing missing data will be proposed and explored. The effect of these methods on the final predicted crash counts will also be quantified. Second, techniques for expanding analysis to an entire road network will be developed. Third, the developed models will be tested on additional datasets in Montreal and Toronto. The ability to predict levels of safety with mobile sensor data is a substantial contribution to the field of transportation.