• François Laviolette

    Professor
    Faculté des sciences et de génie
    Université Laval

    François Laviolette is a full professor at Department of Computer Science and Software Engineering at Université Laval, director of the Big Data Research Center (BDRC) at Université Laval, holder of Canadian Institute for Advanced Research (CIFAR-AI) Chair on Interpretable Machine Learning in Artificial Intelligence (2020-2025), holder of Canadian industrial NSERC chair, Machine Learning for Insurance (2018-2023), member of the scientific committees of the PULSAR project, the VALERIA platform and the Intelligence and Data Institute (IID). At the national and international level, he is an associate member of the MILA Institute, member of the artificial intelligence (IA)/health committee of the Fonds de Recherche du Québec (FRQ), the scientific committee of the DATA AI Institute in France and the AI expert committee of the Observatoire international sur les impacts sociétaux de l’IA et du numérique (OBVIA) at Université Laval.

    In 1984 he obtained a bachelor's degree in mathematics, in 1987 a master's degree and in 1997 a Ph.D. degree in mathematics from the University of Montreal.

    His research interests are focused on artificial intelligence especially machine learning, learning theory, interpretable AI, graph theory, automated verification and bioinformatics.

    Professor François Laviolette is a leader in PAC-Bayesian theory, a branch of learning theory that provides a better understanding of machine learning algorithms and to design new ones. He is interested, among others, in those that solve new types of learning problems, especially those related to genomics, proteomics, drug discovery, etc. He is also interested in making artificial intelligences interpretable in order to better integrate systems where humans are in the decision loop.

    With his expertise Professor François Laviolette plays a significant role in the realization of several multidisciplinary projectsin the Big Data Research Center (BDRC) in insurance, health, bioinformatics and life science, ethics and social acceptability, ... Recently, he focused on innovation in the aerospace industry by co-leading an international project (DEpendable & Explainable Learning) in collaboration with partners from the academic research community and industry with a significant national and international budget ($7.5M and $40M respectively). This project aims to use the scientific basis for what should be a certifiable AI when embedded in a critical system.

    Google Scholar

    • Title of the research project
    Role of machine learning in prostate cancer magnetic resonance imaging radiotherapy target definition
    Description

    The clinical and economic burden of prostate cancer in Canada is substantial and is rising. It has been indicated that 1 in 7 men will develop prostate cancer during their lifetime, and another 1 in 27 will die due to the prostate cancer. However, only a part of prostate cancer cases is clinically important which make the prostate cancer case discrimination crucial to avoid over-treatment.

    Title of the research project
    Development of an in-process quality monitoring technology for plants during pharmaceutical manufacturing using high throughput mass spectrometry coupled to machine learning approaches
    Description

    Process efficacy and robustness are crucial to assure productivity and predictability in pharmaceutical manufacturing. Medicago’s vaccine manufacturing technology uses plants for production and our aim is to develop a system capable of predicting and monitoring plant’s fitness for production early in the process, from plant seedling to harvest of producing leaves.

    Title of the research project
    Machine learning for digital diagnostics of antimicrobial resistance
    Description

    The discovery of antimicrobial agents was one of the great triumphs of the 20th century. The sobering news is that antibiotic resistance was part of the process as well. If nothing is done by 2050, antimicrobial resistance  (AMR) will cost $100 trillion with 10M people/year expected to die (https://amr-review.org). Factors driving AMR extend beyond human healthcare with implications in veterinary medicine, agriculture and the environment (the One Health approach).

  • Élina Francovic-Fontaine

    Ph.D. candidate
    Faculté de médecine
    Université Laval

    Student
    Directeur.e(s) de recherche
    Jacques Corbeil
    Pascal Germain
    Elsa Rousseau
    Start date
    Title of the research project
    Development of an in-process quality monitoring technology for plants during pharmaceutical manufacturing using high throughput mass spectrometry coupled to machine learning approaches
    Description

    Process efficacy and robustness are crucial to assure productivity and predictability in pharmaceutical manufacturing. Medicago’s vaccine manufacturing technology uses plants for production and our aim is to develop a system capable of predicting and monitoring plant’s fitness for production early in the process, from plant seedling to harvest of producing leaves.

    To this end, we must identify the factors that regulate the production level for each plant. We plan to measure a great deal of molecules, called metabolites, to determine the optimal conditions for the plants to generate the maximal amount of each product. Since the quantity of measurements is large, we will use machine learning to design an artificial intelligence capable of understanding and identifying the potentially highly complex patterns of metabolites and/or biomarkers that are correlated with the optimal production.

  • Elsa Rousseau

    Postdoc fellow
    Faculté de médecine
    Université Laval

    Student
    Directeur.e(s) de recherche
    Jacques Corbeil
    François Laviolette
    Start date
    Title of the research project
    Machine learning for digital diagnostics of antimicrobial resistance
    Description

    The discovery of antimicrobial agents was one of the great triumphs of the 20th century. The sobering news is that antibiotic resistance was part of the process as well. If nothing is done by 2050, antimicrobial resistance  (AMR) will cost $100 trillion with 10M people/year expected to die (https://amr-review.org). Factors driving AMR extend beyond human healthcare with implications in veterinary medicine, agriculture and the environment (the One Health approach). New and improved approaches for tackling AMR include better surveillance; rational drug use, different business model for generating antibiotics, innovation at all levels and most importantly a global approach.

    This transnational team grant proposal is tasked to apply new machine learning approaches for modelling AMR for faster diagnosis, better surveillance and prediction of resistance emergence. Specifically, we will develop machine learning implementation that can orient the selection of treatments by assessing the level of resistance, provide rational for the generation of novel antibiotics, and assist in the surveillance of human and livestock AMR around the globe.

    To achieve this, we have assembled a transnational team (Canada, China, Finland, France) with complementary skills with demonstrated expertise in machine learning applied to both genomics and metabolomics and AMR domain experts. Our transnational team has all the elements to be highly impactful and to continue collaborating well past the JPIAMR funding period.

    The complementarity of our expertise will help us to tackle the challenges ahead and ensure our continued success. 

    • Title of the research project
    Robust data pipelines in radiation oncology
    Description

    This project consists of establishing good practices in health data management and building a software infrastructure in order to apply them.

    We have developed pipelines that allow daily recovery of brachytherapy treatment data in order to calculate and store their dosimetric indices in a database dedicated to research. These indices are essential for planning radiotherapy treatments and for estimating their quality.

    Title of the research project
    Automated extraction pipelines in medical imaging
    Description

    The objective of this project is to extract a set of relevant data from the files produced by medical imaging devices.

    The process consists of building ETL (extract-transform-load) pipelines to make the data consumable for analysis and visualization. An example of analysis consists in observing the trend in dose administered to patients according to the establishment, protocol or device used, in order to possibly identify non-standard practices.

    Title of the research project
    Proof of concept for the development of a decision support tool allowing approval exceptional medications through artificial intelligence
    Description

    An exception drug is a drug that is not usually covered by the public drug insurance plan (RPAM). The measures implemented at the RAMQ for exceptional drugs allow the entire population to obtain coverage for certain drugs if they are used in compliance with the indications recognized by the Institut national d'excellence en santé et services sociaux (INESSS). Exception drugs are now a large and constantly increasing part of total spending on prescription drugs.

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    Featured project

    Intraoperative analgesic treatment decisions based on the NOL index: contribution of a data-based approach to improve accuracy and relevance

    Student member : Marzieh Ghiyasinasab

    This research project is based on the analysis of massive data on the NOL index and other intraoperative clinical parameters used by anesthesiologists during surgery. These parameters help them make analgesic treatment decisions in a non-communicating patient under general anesthesia and in whom it is impossible to assess pain and analgesic needs by standard questionnaires performed on awake patients. 
    First, the objective is to interpret the values of this index in relation to the decisions made by the clinician. 

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