Aude Motulsky is a professor at the School of Public Health at the University of Montreal (Department of Health Management, Evaluation and Policy) and a research scientist at the University of Montreal’s Academic Health Centre. She is the Associate Director of the Digital Health Consortium and Co-Director of LabTNS – Digital Transformation in Health. Her research focuses primarily on analyzing the adoption, implementation, and impact of digital health records, such as electronic medical records (EMRs), computerized clinical records (CCRs), the Quebec Health Record (DSQ), and related functionalities. She is particularly interested in the factors that facilitate and limit the use and usability of these tools, and the interpretability of clinical data shared throughout the patient care pathway. All of this is done with the aim of developing analytical capabilities for clinical, managerial, and research purposes. Her work also focuses on technologies designed to optimize medication use, including electronic prescribing and computerized comparative drug assessments. She is also interested in semantic interoperability and terminological standards, particularly in relation to the use of medicines, their method of use, and their efficacy and safety profile.
Martin Vallières is an assistant professor in the Department of Computer Science at the University of Sherbrooke (April 2020). He obtained a PhD in Medical Physics from McGill University in 2017 and completed postdoctoral training in France and the United States in 2018 and 2019. Martin Vallières’ primary research focus is on developing clinically actionable models to better personalize cancer treatments and care (“precision oncology”). He is an expert in radiomics (high-throughput quantitative analysis of medical images) and machine learning in oncology. Throughout his career, he has developed numerous predictive models for various types of cancer. A Canada Research Chair in Artificial Intelligence, Martin Vallières is currently dedicating a significant portion of his work to developing a solution for the integrative modeling of data in oncology. He leads the development of MEDomicsLab, an open-source, end-to-end computing platform for precision oncology. This platform will integrate heterogeneous data from hospitals using deep learning and machine learning methods based on graph theory. By contributing to the improvement of predictive models in oncology, MEDomicsLab will become a key artificial intelligence tool in medicine. Martin Vallières is also a member of the Mila artificial intelligence research institute.
Bouchra Nasri is a member of the Faculty of Biostatistics in the Department of Social and Preventive Medicine at the University of Montreal. Professor Nasri holds a FRQS Junior 1 Fellowship in Artificial Intelligence in Health and Digital Health and is a Principal Investigator on NSERC and CIHR-funded grants in Theoretical Statistics for Complex Data and Mathematical Modelling for Infectious Diseases. She is co-lead of the Data Management Theme (2021-2024) of the One Health Modelling Network for Emerging Infections (OMNI) and a member of the NSERC-PHAC-funded Mathematics for Public Health (MfPh). Since March 2023, she has been Chair of PathCheck’s Data Informatics Center of Epidemiology, and since 2024, she has been Co-Director of the Digital Health Network. Professor Nasri is the author and co-author of several articles on time series, dependency modeling, multivariate statistics, mathematical modeling of infectious diseases, text mining, and evidence synthesis.
Philippe Després is a full professor in the Department of Physics, Engineering Physics, and Optics at Université Laval. He is also a member of the Université Laval Cancer Research Centre , a medical physicist at the CHU de Québec-Université Laval , and a researcher at the Quebec Heart and Lung Institute-Université Laval (IUCPQ-UL). After earning a master’s degree in Physics from Université Laval (2000) and a doctorate in Physics from Université de Montréal (2005), he completed a postdoctoral fellowship (2005-2007) at the University of California, San Francisco, in the field of biomedical engineering and molecular imaging. His research projects focus primarily on medical imaging, particularly tomographic reconstruction and the use of artificial intelligence for detection and classification. He was a pioneer in high-performance computing on graphics processing units (GPUs), leading to the development of innovative applications in medical physics, including an ultra-fast GPU-based Monte Carlo radiative transport code. He is also interested in data valorization in the medical field, particularly the infrastructure, standards, and best practices (including the FAIR principles) necessary for the responsible use of clinical information. In this capacity, he serves as co-lead of CIRRUS, the
Centre for Integration and Governance of Health Information for Secondary Use at IUCPQ-UL. He is also the director of the Centre for Research in Big Data (CRDM) at Université Laval and a researcher at the
International Observatory on the Societal Impacts of AI and Digital Technology . He sits on the Research Council of the Digital Research Alliance of Canada and directs the NSERC CREATE program in Responsible Data Science in Health (sdrds.org).
