RESEARCHERS INVOLVED
Researchers involved
Biography
Jacques Bélair is Full Professor in the Department of Mathematics and Statistics at Université de Montréal, which he joined in 1983 as an Assistant Research Professor. He had previously obtained a PhD in applied mathematics from Cornell University (supervisor: Philip J Holmes) and worked as an NSERC postdoctoral fellow in the Department of Physiology at McGill University (supervisor: Leon Glass). He has served as deputy director of the CRM and was also vice-dean of the Faculty of Graduate and Postdoctoral Studies; he was President of the Canadian Applied and Industrial Mathematics Society (CAIMS) from 2009 to 2011. In 2019, he co-chaired the Organizing Committee of the Annual Meeting of the Society for Mathematical Biology (SMB).
His research concerns mathematical modelling of dynamic regulatory processes in biology. In the past, he has been interested in various aspects of cardiac arrhythmias and motor control. He is currently studying the control of blood cell production (hematopoiesis) and associated pharmaceutical interventions, and the propagation of infectious diseases in general, and COVID-19 in particular. He is researcher affiliated with the Centre for Disease Modelling (CDM) based at York University and the Centre for Applied Mathematics in Bioscience and Medicine (CAMBAM) of McGill University.
Marie-Claude Boily
Biography
Marie-Claude Boily is Professor in Mathematical Epidemiology at Imperial College London and an affiliated researcher at Centre de recherche du CHU Québec – Université Laval.
Her research focuses on the epidemiology and prevention of infectious diseases, such as human immunodeficiency virus (HIV) and sexually transmitted infections, in various settings using a combination of mathematical modelling, statistical, and empirical methods. She aims to inform public health decision making and develop the field of mathematical modelling. Her team has been involved in studies to understand the transmission dynamics of infection and maximise the impact of various prevention tools (e.g. HIV treatment, vaccination, structural interventions), on HIV, Human papillomavirus (HPV) and herpes simplex virus (HSV) infections and diseases in the population. She also recently started to work on COVID-19, and currently is leading the National Institutes of Health (NIH) funded HIV Prevention Trial Network (HPTN) Modelling Centre, which has the mission of designing and conducting mathematical modelling and computer simulation studies to inform HPTN research activities.
Her studies, which strive to use the best epidemiological and clinical data, are often conducted in collaboration with multi-disciplinary and international collaborators, which help to translate her results into public health recommendations and actions locally and globally.
David Buckeridge
Biography
David Buckeridge is Associate Professor at the Department of Epidemiology, Biostatistics and Occupational Health at McGill University. He holds a M.D. from Queen’s University, a M.Sc. in Epidemiology from the University of Toronto, a Ph.D. in Biomedical informatics from Stanford University. He is a Fellow of the Royal College of Physicians and Surgeons of Canada with specialty training in Public Health and Preventive Medicine. He is a medical consultant for the Institut National de la Santé Publique du Québec and the Institut National d’Excellence en Santé et en Services Sociaux du Québec. David Buckeridge also chairs the CIHR Institute Advisory on Health Promotion and Prevention.
For his research, he uses methods from biomedical informatics, computer science, epidemiology, biostatistics, and behavioral science to develop and evaluate the impact of software technologies that use Big Data to monitor population health and health systems, and to feedback information to guide the actions of consumers, health professionals, and decision makers. Previous and ongoing work includes the development of statistical methods for outbreak detection and the use of simulation modeling to evaluate surveillance systems.
In his clinical and knowledge translations activities, he advises governments in Canada and internationally regarding the implementation and effective use of evidence-based software technologies for health monitoring. Through this work, he has developed and implemented innovative software systems in Montreal and for the province of Quebec and has helped the Public Health Agency of Canada to define and evaluate their surveillance mandate. He has also contributed to the development of nationwide surveillance systems by the US and the Chinese Centers for Disease Control (CDC), and advised the European CDC on how to effectively use new technologies for health monitoring.
Arthur Charpentier
Biography
Arthur Charpentier is Full Professor at the Université du Québec à Montréal (UQAM). He obtained his Ph.D. in Mathematics in 2006 from the Catholic University of Leuven in Belgium, was a Professor in the Faculty of Economics of Université Rennes 1 in France from 2008 to 2018 and was the director of the Data Science for Actuaries Program at the Institut des Actuaires in Paris, France.
He is working on risk modeling and insurance data. He published Computational Actuarial Science with R (CRC, 2014) and is the editor of the academic blog Freakonometrics (https://freakonometrics.hypotheses.org/). His recent researches are on machine learning and reinforcement learning. He worked on optimal control (on lockdown and testing) in the context of COVID-19 (https://doi.org/10.1101/2020.05.13.20100842).
Morgan Craig
Biography
Morgan Craig is Assistant Professor in the Department of Mathematics and Statistics at the Université de Montréal and a Researcher at the Sainte-Justine University Hospital Research Centre (CRCHUSJ) in the Immune Disorders and Cancer group. Morgan Craig’s expertise is in quantitative medicine, a discipline integrating computational biology, (patho) physiology, and pharmaceutical sciences with traditional wet-lab and clinical biomedical approaches. The principal objective of her research is to tailor therapeutic interventions using mathematical modelling as complementary means to uncover the mechanisms that underlie healthy and pathological states and the dynamical evolution from one state to the other. She has previously developed predictive models in a variety of cancer applications that have been leveraged to tailor immunotherapies and reduce toxic side effects. A specific focus of her research is understanding hematopoietic crosstalk and resulting innate immune response.
Morgan Craig and her team, including in particular, the postdoctoral fellow Adrianne Jenner and student Sofia Alfonso, are focused on understanding differential immune responses in COVID-19 through multiscale mathematical models of the systemic immune response and localized infection dynamics in tissues. This work is carried out in collaboration with experimentalists and clinicians at CRCHUSJ and the SARS-CoV-2 Tissue Simulation Coalition (http://physicell.org/covid19/).
Simon de Montigny
Biography
Simon de Montigny holds a doctorate in mathematics from Polytechnique Montréal. He is an Assistant Research Professor in the Department of Social and Preventive Medicine at the School of Public Health at the Université de Montréal, and a Researcher at CHU Sainte-Justine. His research in fundamental artificial intelligence focuses on learning artificial neural network based on postsynaptic signals. At the postdoctoral level, he developed simulations of experimental Human immunodeficiency virus (HIV) vaccines within the HIV Vaccine Trials Network, and he collaborated with the HIV Prevention Trials Network Modeling Center to model the deployment of antiretroviral treatment programs and progress towards targets. 90-90-90 from UNAIDS and World Health Organization (WHO).
More specifically his research focuses on the use of artificial intelligence for big data analysis in precision medicine and public health. In epidemiology of infections, it aims to design methods and tools to facilitate the generation of transmission models, the simulation of epidemics and the effect of public health interventions, and the automated and real-time integration of field data into these models and simulations.
Hélène Guérin
Biography
Hélène Guérin is Professor in the Department of Mathematics at the Université du Québec à Montréal (UQAM). She obtained a PhD in statistics and mathematical probabilities from the University of Nanterre in 2002. From 2003 to now, she has been a Lecturer at the University of Rennes 1. She was a researcher in Applied Mathematics, specializing in probability at the Centre national de la recherche scientifique (CNRS) from 2013 to 2014.
Her main areas of expertise concern the Markov model, Piecewise Deterministic Markov Processes, the Lévy Process, Stochastic processes and Ruin Theory. She is the author of more than 15 publications.
Guillaume Lajoie
Biography
Guillaume Lajoie is an assistant professor at the Department of Mathematics and Statistics at the Université de Montréal (UdeM) since 2018. He holds a Canada CIFAR AI chair, and he is an FRQS Research Scholar. He obtained his PhD in Applied Mathematics from the University of Washington, in Seattle. He is an executive committee member of UNIQUE: Unifying Neuroscience and artificial Intelligence Québec, and a Core Academic member of Mila, the Québec Artificial Intelligence Institute.
His research focuses on the interactions and commonalities of biological and artificial neural computations. His research group works at the intersection of AI and Neuroscience, developing tools to better understand neural networks as well as algorithms for brain-machine interfaces for scientific and clinical use. His work is motivated by the remarkable ability of neural networks (biological and artificial) to learn and support complex, emergent computations. He uses tools from dynamical systems, information theory, statistics and machine learning to address a range of problems, in collaboration with experimental neuroscientists and machine intelligence researchers..
Patrick Leighton
Biography
Patrick Leighton is Professor of Epidemiology and Publich Health at the Faculty of Veterinary Medecine, at the Université de Montréal, and an active member of the Epidemiology of Zoonoses and Public Health Research Group (GREZOSP). His research focuses on the ecology of wildlife diseases that are transmissible to humans, and in particular the impact of ecological change on the epidemiology of these diseases and the risk they pose to public health. He codeveloped and co-directs U. Montreal’s Master’s Program ine One Health and Veterinary Public Health.
Andrea Lodi
Biography
Andrea Lodi received his PhD in System Engineering from the University of Bologna in 2000 and he has been Herman Goldstine Fellow at the IBM Mathematical Sciences Department, NY in 2005-2006. He has been Full Professor of Operations Research at the Department of Electrical, Electronic and Information Engineering (DEI) at University of Bologna in Italy, between 2007 and 2015. Since 2015, he is holder of the Canada Excellence Research Chair in “Data Science for Real-time Decision Making” at Polytechnique Montréal. His main research interests are in Mixed-Integer Linear and Nonlinear Optimization and Data Science and his work has received several recognitions including the IBM and Google faculty awards. He is author of more than 100 publications in the top journals of the field of Mathematical Optimization and Data Science.
A large portion of Andrea Lodi’s applied work is in Healthcare with contributions in kidney transplantation, operating and emergency room scheduling and management, forecast and allocation of healthcare staff needs. He has been network coordinator and principal investigator of two large EU projects/networks, and, since 2006, consultant of the IBM CPLEX research and development team. Andrea Lodi is the co-principal investigator of the project “Data Serving Canadians: Deep Learning and Optimization for the Knowledge Revolution”, funded by the Canadian Federal Government under the Apogée Programme and scientific co-director of the Montréal Institute for Data Valorization (IVADO).
Mathieu Maheu-Giroux
Biography
Mathieu Maheu-Giroux holds the Canada Research Chair in Population Health Modeling. He is Assistant Professor in the Department of Epidemiology, Biostatistics and Occupational Health (EBOH) at McGill University. He previously worked as a researcher at Imperial College London after completing his Doctorate in Science at Harvard T. H. Chan School of Public Health (2015) and a postdoctoral training in Mathematical Epidemiology at Imperial College London.
Mathieu Maheu-Giroux has international field experience in Tanzania, Peru and Chile and has published in journals such as the International Journal of Drug Policy, AIDS and Behavior and the American Journal of Epidemiology. His work focuses on mathematical modeling, the epidemiology of infectious diseases and impact assessments and cost-effectiveness analyzes.
Benoît Mâsse
Biography
Benoît Mâsse is Professor in Biostatistics at the Université de Montréal since 2010 and joined the Research Center at CHU Ste-Justine. Previously, he worked for 11 years at the University of Washington and the Fred Hutchinson Cancer Research Center. In 2006, he received a 7-year grant of over $US 25 millions from the National Institutes of Health (NIH) as the Principal Investigator responsible for establishing the Statistical Data Management Center for providing support to all international Phase I-II-III trials and observational studies conducted within the Microbicide Trials Network. Currently, he is directing the Applied Clinical Research Unit (URCA) at CHU Ste-Justine where he has established the infrastructure to support the conduct of clinical and observational studies.
He was the lead senior statistician for the landmark HPTN 052 trial and contributed significantly to the unique design of this trial. HPTN 052 is the first randomized clinical trial to show that treating an Human immunodeficiency virus (HIV)-infected individual with antiretroviral therapy (ART) can reduce the risk of sexual transmission of HIV to an uninfected partner. Based on these results, the HPTN 052 study was named Scientific Breakthrough of the Year in 2011 by Science magazine. According to Science, the study was selected for its “profound implications for the future response to the AIDS epidemic.”
In 2014, Benoît Mâsse was chairing the International Data and Safety Monitoring Board (DSMB) for the Ebola vaccine trial in Guinea (Ebola: Ça Suffit). This was the first trial that showed the efficacy of the rVSV-ZEBOV vaccine. Currently, he is chairing the International DSMB for the vaccine expanded access study for the ongoing Ebola epidemic in the Democratic Republic of the Congo. In December 2019, the National Geographic selected the HPTN 052 and the Ebola: Ça Suffit trials as the top 20 discoveries of the last decade (all research field combined).
Erica E. M. Moodie
Biography
Erica E. M. Moodie obtained her MPhil in Epidemiology in 2001 from the University of Cambridge, and a PhD in Biostatistics in 2006 from the University of Washington, before joining McGill University where she is now a William Dawson Scholar and Associate Professor of Biostatistics. Her main research interests are in causal inference and longitudinal data with a focus on adaptive treatment strategies. She is an Elected Member of the International Statistical Institute, and an Associate Editor of Biometrics. She holds a Chercheur-Boursier senior career award from the Fonds de recherche du Québec-Santé. She is the recipient of the 2020 CRM-SSC Prize in Statistics.
She is working with a team led by Nicole Basta on public awareness of SARS-Cov-2 vaccine development, and with the Canadian Longitudinal Study on Aging to understand the interplay of frailty and SARS-CoV-2 infection among the elderly.
Biography
Manuel Morales is currently an Associate Professor in the Department of Mathematics and Statistics at the University of Montreal.
He specializes in financial and actuarial mathematics and his current research interests are in machine learning applied to banking and responsible investment. His other general areas of interest are mathematical finance and risk theory.
Specifically, his current topics of interest go beyond classical ruin theory, ranging from new applications of learning algorithms in finance and banking to micro-structural modeling in high frequency finance.
Most recently, he has been involved in the National Bank of Canada’s artificial intelligence transformation initiative. As Chief Scientist for AI, he led the scientific efforts of the bank’s strategy to leverage AI technologies across all verticals. She had the opportunity to work on a wide variety of projects ranging from wealth management to retail banking applications. He also led the early work to establish an AI governance framework.
Since 2018, he has been the managing director of the Fin-ML Network.
Bouchra Nasri
Biography
Bouchra Nasri is an Assistant Professor in the Department of Social and Preventive Medicine at the Université de Montréal. She had obtained her PhD in Statistics at the INRS-ETE in 2017. She was a postdoctoral fellow in Statistics at McGill University in 2020. She is a member of the Réseau de recherche en santé des populations du Québec (RRSPQ) and the Centre de recherche en santé publique (CReSP)
Her area of expertise is statistics and its applications. In statistics, she is mainly interested in univariate and multivariate time series modeling, dependence models, extreme value theory, spatio-temporal models and regression. The main applications of her research focus on the socio-economic impacts of climate change on public health and infectious diseases.
Louis-Martin Rousseau
Biography
Louis-Martin Rousseau is a Full Professor at Polytechnique Montréal and holder of a Canada Research Chair in Healthcare Analytics and Logistics. His research focuses on solving complex routing problems, personnel rostering problems, as well as integrated decision problems which appear in supply chain both in retail and in healthcare. To address theses issues, he puts forward hybrid methodologies based on constraint programming and classical operations research. As the field of personalized medicine emerges, it is believed that there will be enormous challenges in the interaction between individual treatment planning and execution in terms of limited and expensive medical resources. In particular, Louis-Martin Rousseau is conducting research in areas where population aging is going to have a big impact, namely: cancer treatment, homecare services and hospital logistics.
Alexandra M. Schmidt
Biography
Alexandra M. Schmidt is Associate Professor of Biostatistics in the Department of Epidemiology, Biostatistics and Occupational Health (EBOH) at McGill University. She is currently the Program Director of the Biostatistics Graduate Program of EBOH.
She is Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute. In 2017 she was awarded the Distinguished Achievement Medal from the American Statistical Association’s Section on Statistics and the Environment. In 2008, she received the Abdel El-Shaarawi Young Investigator Award, from The International Environmetrics Society. She was President of the International Society for Bayesian Analysis (2015).
Her main area of research is on the development of statistical models for spatio-temporal processes under the Bayesian framework. Her main projects involve the development of models for multivariate spatio-temporal processes that preclude the use of any transformation to attain normality. She is also developing models to understand the spread of cases of dengue fever, Zika, and chikungunya across the neighborhoods of Rio de Janeiro during the first joint epidemic the city experienced between 2015 and 2016.
David A. Stephens
Biography
David A. Stephens is Full Professor at the Department of Mathematics and Statistics, McGill University since 2006. He obtained a Ph.D. In Statistics from the University of Nottingham, UK in 1990 under the supervision of Prof. Adrian F. M. Smith. He was the Chair of the Department of Mathematics and Statistics from 2015-2019 and he is the Vice-Dean in the Faculty of Science since April 2019.
His research interests are Bayesian statistics: methodological and computational methods. Specific areas of interest include bioinformatics, biostatistics and time series analysis.
Biography
Luc Vinet is Aisenstadt Professor in the Department of Physics at the Université de Montréal. Since July 2021, he has been the Executive Director of IVADO. Luc Vinet was the Director of the Centre de recherches mathématiques (CRM) from 1993 to 1999 and from 2013 to 2021.
Born in Montreal, Luc Vinet holds a PhD from the Université Pierre et Marie Curie in Paris and a PhD from the Université de Montréal, both in theoretical physics. After two years as a research associate at MIT, he was appointed assistant professor in the Department of Physics at the Université de Montréal in the early 1980s and promoted to Full Professor in 1992. His work focuses on the exact solution of physical models through the study of symmetries and their description in terms of algebraic structures.
In 1999, Luc Vinet joined McGill University where he is Vice Principal (Academic) and Provost. From 2005 to 2010, he was Rector of the Université de Montréal, one of Canada’s leading universities. He currently chairs the Board of Directors of Fulbright Canada and is a member of the Board of the National Institute for Nanotechnology in Edmonton. His many honors include the 2009 Armand-Frappier Award from the Government of Quebec and the 2012 ACP-CRM Prize in Theoretical and Mathematical Physics. In addition, he holds an honorary doctorate from the Université Claude-Bernard de Lyon.
In 2017, Professor Vinet was awarded the rank of Officer of the National Order of Quebec by the Prime Minister Philippe Couillard. Then in 2018, he was named a fellow of the AMS.
Biography
Guy Wolf is an Assistant Professor in the Department of Mathematics and Statistics at the Université de Montréal and researcher at the Montreal Institute for Learning Algorithms (Mila-Quebec Institute for Research in Artificial Intelligence).
His research focuses on the analysis of exploratory data with applications in Bioinformatics. His approaches are multidisciplinary and combine Machine Learning, signal processing, harmonic analysis, and applied math tools. In particular, his recent works use a combination of diffusion geometries, manifold learning, and deep learning to find emergent patterns, dynamics, and structure in big high dimensional data (e.g., in single-cell genomics and proteomics). As part of the Mila “AI against COVID-19” task force, he is involved in cutting-edge projects relating to the analysis of genomic (and multi-omic) data to understanding the virus operations, interaction with host biology, and targeting potential antiviral treatments.
His research interest concern: Exploratory data analysis with Manifold Learning and Deep Learning; Applied harmonic analysis, Spectral Graph Theory, and diffusion geometry, Graph signal processing and geometric Deep Learning; Data-driven characterization of nonlinear structures, patterns, and dynamics; Biomedical Big Data applications (e.g., genomics and neuroscience).
Biography
Mamadou Yauck is an Assistant Professor of Statistics at the Université du Quebec à Montréal (UQAM). He had obtained a PhD in Statistics from the Université Laval in 2019. He was a Postdoctoral Researcher at McGill University from 2019 to 2021. He was also a Postdoctoral Research Associate for the UNC Gillings School of Global Public Health.
Professor Yauck’s volunteer experiences includes serving as Vice President of the Children’s Parliament, President of the EDEN Club Network since 2005, and Representative for West and Central Africa for Unicef since 2006.
Finally, he has received awards and disctinctions as a Star Professor in 2019, was recipient of the Student Research Presentation Awards Winner 2016, laureate of the Leadership and Sustainable Development Scholarship in 2014 and winner for the best student in Senegal competition in 2010.
His research interests concern: Capture-Recapture, Respondent-driven sampling, Network Data Analysis, Causal inference and Computational Statistics.
