Postdoctoral Scholarships

 Postdoctoral Scholarships Program

 

IVADO’s commitment to equity, diversity and inclusion and note to applicants
To ensure all members of society draw equal benefit from the advancement of knowledge and opportunities in data science, IVADO promotes equity, diversity and inclusion through each of its programs. IVADO aims to provide a recruitment process and research setting that are inclusive, non-discriminatory, open and transparent.

Overview

Description

FAQ

Application

Results - Winter 2018 Contest

Results - Summer 2018 Contest

Program description

  • Field of study: The IVADO Postdoctoral Scholarship Funding program supports research on the issues raised in the Canada First funding competition: data science in a broad sense, including methodological research in data science (machine learning, operations research, statistics) and its application in a range of sectors including the priority sectors of IVADO (health, transportation, logistics, energy, business and finance) or any other sector of application (sociology, physics, linguistics, engineering, etc.).
  • Amount of salary (not an award) and grant period:
    • Regular scholarship: $70 000 per year for regular funding (including benefits) for up to two years, entirely funded by IVADO.
    • Partnership scholarship: $70 000 per year equally funded by IVADO ($35 000) and a partner ($35 000) for partnership funding (including benefits) for up to two years.
    • “Fellow” scholarship: $90 000 per year for a scholarship (including benefits) for up to three years, entirely funded by IVADO. In the first year, an additional $10 000 may be awarded upon request to cover relocation expenses, along with $15 000 per year to fund research activities.
    • Upon request, applicants from countries eligible to receive ODA from Canada may be reimbursed for their relocation expenses to begin their postdoctoral training in Montréal.
  • Application deadline: November 25, 2018
  • Expected application notification date: Early January 2019
  • Criteria: See description tab
  • Submission: See submission tab
  • Information: programmes-excellence@ivado.ca

Program objectives

  • Train future researchers, professors and, more broadly, future data science stakeholders, primarily in the areas of expertise of IVADO members: operations research, machine learning and decision science.
  • Promote the mobility, recruitment and retention of young researchers.
  • Foster the development of collaborative and applied cutting-edge research.

Eligibility

Postdoctoral scholarship applicants must:

  • have earned their first doctorate fewer than five years prior to the date on which they are applying or intend to earn their first doctorate by the date on which the competition results are announced (December, 2018). IVADO will be flexible with applicants who provide an adequate explanation for a career interruption or particular circumstances. This explanation must be included in the application (e.g. pregnancy/maternity or sick leave);
  • intend to attend HEC Montréal, Polytechnique Montréal, Université de Montréal, McGill University or University of Alberta;
  • Priority will be given to candidates who have had their main affiliation outside Montreal for the past year. This measure aims to promote the attraction of talented international researchers.

Professor (supervisor) applicants must:

  • hold a faculty position as a professor at HEC Montréal, Polytechnique Montréal or Université de Montréal;
    • Professors at the University of Alberta and McGill University may act as supervisors providing they are members of IVADO (MILA, CIRRELT, GERAD, CERC Data Science, CRM, Tech3Lab).
  • not have acted as the applicant’s PhD supervisor or co-supervisor;
  • only submit one application to the competition.

 

Funding period

The funding period starts between January and March 2019.

Amounts and terms

The funds (salary and reimbursement of relocation expenses) shall be transferred to the office of research of the supervisor’s university, and the university shall pay the postdoctoral researcher according to its own compensation rules. For projects that require ethics approval, the funds shall only be paid out once the approval is granted. Some projects, including partnership projects, may require specific agreements (e.g. pertaining to intellectual property).

Funding may be cut, withheld, delayed or rescinded under the circumstances outlined in the letter of award.

Competitive process

Review and criteria

The applications shall be reviewed to ensure compliance with program rules (e.g. applications that are incomplete, exceed the page limit or list an ineligible applicant or supervisor). Only the applications that meet all criteria will be forwarded to the review committee.

The parity-based review committee shall be made up of professors from one of the following institutions: HEC Montréal, Polytechnique Montréal, Université de Montréal or McGill University. Committee members shall be well-read in IVADO’s areas of expertise and shall not be listed as a supervisor by any applicant. However, given the small size of the communities in certain areas, it may prove difficult to select expert reviewers who are not included in an application submitted to the competition. In such cases, a reviewer may be required to assess an application despite being listed in another application as a supervisor. An external reviewer may also join the committee. The committee shall ensure by all possible means that the reviewer does not influence the ranking of the application in which he/she is included.

The review committee shall then rank the applications based on excellence, as well as the project’s alignment with IVADO’s overarching framework, which aims to promote multidisciplinary collaboration and diversity in data science. In terms of excellence, the committee will specifically assess:

  • the applicant’s contributions to research (scientific impact, quality of research, productivity and previous funding)
    the quality of the applicant’s doctoral thesis and his/her academic excellence
  • the extent and scope of the applicant’s experience (multidisciplinary and professional experiences, extra-academic activities, collaborations, contributions to the scientific community and society as a whole, etc.)
  • he alignment of the applicant’s experience and proposed project

Applicants who apply for partnership funding will also be ranked based on their projects’ applied research potential.

The review committee will rank the applicants seeking regular and partnership funding. While in session, the review committee may award a Fellow scholarship to outstanding applicants seeking regular funding.

Final step and commitments

The postdoctoral researcher shall:

  • be physically present at his/her supervisor’s university or share his/her time between the home university and partner organization (for partnership funding);
  • contribute to IVADO’s community and activities by, for example, taking part in:
    • presentations on his/her research;
    • training and knowledge dissemination activities;
      consultations;
    • activities generally undertaken by career researchers (mentorship, assessment, co-organization of events, etc.);
  • recognize that he/she is a member of an academic community to which he/she shall contribute;
  • comply with the Tri-Agency Open Access Policy on Publication. Postdoctoral researchers are encouraged to publish their research findings (papers, recordings of presentations, source codes, databases, etc.), in compliance with the intellectual property rules that apply to their own specific case;
  • recognize the financial support granted by IVADO and the CFREF or FRQ when disseminating the research results and, more broadly, in all the activities in which he/she takes part

The supervisor shall:

  • provide a work environment that is conducive to the completion of the project
  • oversee the work of the postdoctoral researcher

FAQ

  • What do you mean by “Priority will be given to candidates who have had their main affiliation outside Montreal in the last year.”?

    • The goal of this program is to attract talented young researchers to Montreal. We therefore primarily target students who are not already established in Montreal.

    • Thus, with similar applications, a student or postdoctoral researcher enrolled full-time in a Montreal university would be ranked lower than a student who would arrive from outside Montreal.

  • Is there a particular format for preparing a CV?
    • No, there is no particular format that needs to be followed.
  • Are there any specific rules for the recommendation letter?
    • No, there are no specific rules for the recommendation letter.
  • Should I include the recommendations in the PDF file?
    • No, the recommendations must be submitted by the individuals who sign them.
  • Can candidates send recommendation letters themselves?
    •  No, recommendation letters can only be sent by the author of the letter. All letters should be sent to programmes-excellence@ivado.ca (a recommendation letter cannot be sent by the candidate).
  • Can I send my unofficial transcript?
    • No, you must send us your official transcript including all your current results. Originals or certified copies must be scanned and attached to the application and for non-Canadian universities, you must specify the rating scale.
  • I earned my PhD in a country that does not provide a transcript. What document(s) should I include instead?
    • Please include a note in your application and provide the transcript for your master’s degree.

Partnership funding

  • Is it possible to apply for partnership funding without having a partner at the time of the application?
    • No, unfortunately, you need to find your partner before submitting your application. A letter of commitment from your partner is also required.
  • For partnership scholarships, are there any specific rules for writing the confirmation letter from the partner and by whom should it be written?
    • The confirmation letter must be written by the project supervisor and must contain the subject/title of your project and the amount of their financial commitment ($ 35 000/year for two years).

Didn’t find what you were looking for? Send us an e-mail.

All applications must submit:

  • a duly completed application form
  • CV (free format)
  • Ph.D transcripts (as well as information on the grading scale when the transcript is issued by a non-Canadian university)
  • project description (maximum one page)
  • recommendations (a maximum of three), including a letter sent directly from the postdoctoral  supervisor (or potential postdoctoral supervisor) or thesis advisor to programmes-excellence@ivado.ca
  • confirmation from the partner (partnership funding applications only)

Complete applications in a single PDF file must be sent to programmes-excellence@ivado.ca and copied to the supervisor listed in the application.  

  • 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.