M.Sc. candidate
Medical Physics Unit
McGill University
This research project is focused on using blockchain or an alternative solution to provide security for data donation using the Opal app.
It will put in place a demonstrative blockchain infrastructure, examining its challenges and drawbacks and proposing potential innovative solutions.
This research project is focused on using blockchain or an alternative solution to provide security for data donation using the Opal app.
It will put in place a demonstrative blockchain infrastructure, examining its challenges and drawbacks and proposing potential innovative solutions.
This research project is focused on preparing a pilot project for the donation of radiotherapy data by radiotherapy patients using the Opal patient portal.
This project is investigating ways in which patients can share their data and it will put in place the infrastructure for a demonstrative project.
This project involves an examination of the regulatory privacy and confidentiality compliance requirements for the use of a patient portal in various Canadian provinces.
Romina also works as a member of the quality assurance team, the market research team, and assisted with deploying Opal in numerous clinics within the Cedars Cancer Centre.
This project is focused on determining if patient-reported outcomes are better correlated with actual dose delivered than with planned dose in prostate cancer patients receiving radiotherapy.
The project will use daily cone-beam CT images to calculate the daily and total radiation dose delivered to patients, and the Opal app to collect their patient-reported outcomes.
Ph.D. candidate
Medical Physics Unit
McGill University
This project is focused on determining if patient-reported outcomes are better correlated with actual dose delivered than with planned dose in prostate cancer patients receiving radiotherapy.
The project will use daily cone-beam CT images to calculate the daily and total radiation dose delivered to patients, and the Opal app to collect their patient-reported outcomes.
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.
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.
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.
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).
Featured project
Prostate cancer is the second most frequent cancer and the fifth leading cause of cancer death among men. To improve patient outcomes, treatment must be personalized based on accurate prognosis. Nomograms already exist to identify patients at low risk for recurrence based on preoperative clinical information, but these tools do not use patients’ medical images.