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Education

Project Leader(s): 

[url=mailto:[email protected]]Dr. Jean-Marie Dufour[/url] , Université de Montréal

Project team: 
Dr. Marine Carrasco, Université de Montréal
Dr. Jérôme Detemple, Boston University
Dr. Rene Garcia, Edhec Business School
Dr. Silvia Gonçalves, Université de Montréal
Dr. Lynda Khalaf, Université Laval
Dr. Nour Meddahi, Université de Toulouse
Dr. Benoit Perron, Université de Montréal
Dr. Éric Renault, University of North Carolina Chapel Hill
Dr. Marcel Rindisbacher, University of Toronto
Non-academic participants: 
Funding period: 
February 25, 2022 - March 31, 2021

This project deals with the mathematics of risk modeling and resource management. Using mathematical and statistical methods, the team develops new tools to help the financial services industry make better decisions about when to trade and at what price based on the available financial data. During the past year, the team focused on the development of statistical methods for measuring volatility and assessing asset pricing models in financial markets.

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Project Leader(s): 

Dr. Mike Kouritzin , (University of Alberta)

Project team: 
Andrew Heunis, (University of Waterloo)
Bruno Remilard, (HEC Montreal)
Douglas Blount, (Arizona State University)
Pierre Del Moral, (Universite Pal Sabatier)
Jie Xiong, (University of Alberta)
John Bowman, (University of Alberta)
Donald Dawson, (University of Toronto)
Edit Gombay, (University of Alberta)
Jack Macki, (University of Alberta)
Thomas G. Kurtz, (University of Wisconsin at Madison)
Yau Shu Wong, (University of Alberta)
Laurent Miclo, (Universte Paul Sabatier)
Funding period: 
February 25, 2022 - March 31, 2021

This project uses mathematical filtering theory to develop computer tractable real time solutions for incomplete, corrupted information problems. These techniques have proven to be beneficial in defence, communications, media effects, and manufacturing. In 2002-2003, Optovation Inc. was added as a new partner, Lockheed Martin Corp. filed for two new patents and we formed a spin-off company, Random Knowledge Inc. to commercialize our technology in the areas of Network Security, Fraud Detection, and Finance.

Project Leader(s): 

Dr. François Soumis, (École Polytechnique de Montréal)

Project team: 
Dr. Guy Desaulniers Guy, (École Polytechnique de Montréal)
Dr. Pierre Baptiste, (École Polytechnique de Montréal)
Dr. Jacques Desrosiers, (HEC Montréal)
Dr. Alain Hertz, (École Polytechnique de Montréal)
Dr. Sophie D’Amours, (Université Laval)
Funding period: 
April 1, 2021 - March 31, 2021
Project Leader(s): 

Dr. Changbao Wu, University of Waterloo

Project team: 
Dr. Jiahua Chen, University of Waterloo
Dr. David Haziza, Université de Montréal
Dr. Jerry Lawless, University of Waterloo
Dr. Wilson Lu, Acadia University
Dr. Nancy Reid, University of Toronto
Dr. Jamie Stafford, University of Toronto
Dr. Brajendra Sutradhar, Memorial University of Newfoundland
Dr. Roland Thomas, Carleton University
Dr. Roland Thomas, Carleton University
Dr. Zilin Wang, Wilfrid Laurier University
Funding period: 
April 1, 2021 - March 31, 2021

The surveys being developed by government, health and social science organizations have increased in complexity and as a result, the data that is collected is similarly more complicated. Thus, this project focuses on developing new tools to address issues which arise during the analysis of this complex data including longitudinal data, information which is based on a set of repeated observations of an individual, or group of individuals, over time.

Project Leader(s): 

Dr. Yoshua Bengio, Université de Montréal

Project team: 
Dr. Hugh Chipman, Acadia University
Dr. Dale Schuurmans, University of Alberta
Dr. Pascal Vincent, Université de Montréal
Dr. Shai Ben-David, University of Waterloo
Funding period: 
February 25, 2022 - March 31, 2021

Statistical machine learning is an endeavor in which statisticians and computer scientists use computation to identify useful information from large amounts of data. Telecommunications, insurance and pharmaceutical companies use the team’s machine learning and data mining techniques to determine customer patterns, predict future customer behavior and better understand their needs. The project addresses some of the main practical and theoretical difficulties encountered when dealing with large datasets.

Project Leader(s): 

Dr. Anthony Vannelli, University of Guelph & Dr. Miguel F, AnjosEcole Polytechnique

Project team: 
Dr. Abdo Youssef Alfakih, University of Windsor
Dr. Kankar Bhattacharya, University of Waterloo
Dr. Claudio A. Canizares, University of Waterloo
Dr. Richard J. Caron, University of Windsor
Dr. Thomas Coleman, University of Waterloo
Dr. Tim N. Davidson, McMaster University
Dr. Antoine Deza, McMaster University
Dr. Samir Elhedhli, University of Waterloo
Dr. David Fuller, University of Waterloo
Dr. Elizabeth Jewkes, University of Waterloo
Dr. Paul McNicholas, University of Guelph
Dr. Chitra Rangan, University of Windsor
Dr. Tamás Terlaky, Lehigh University
Dr. Stephen Vavasis, University of Waterloo
Dr. Henry Wolkowicz, University of Waterloo
Dr. Guoqing Zhang, University of Windsor
Funding period: 
April 1, 2021 - March 31, 2021

Due to the explosive growth in the technology for manufacturing integrated circuits, modern chips contain millions of transistors. Using sophisticated optimization algorithms, it is possible to achieve notable increases in the performance of the chips, reduce the manufacturing costs, and produce faster, cheaper computing for society. Thus, the objective of this project is to enhance the solution of large-scale optimization problems arising in these applications.

Project Leader(s): 

Dr. Gary F. Margrave, & Dr. Michael Lamoureux, University of Calgary

Project team: 
Dr. Robert Ferguson, University of Calgary
Dr. Peter C. Gibson, York University
Dr. Michael C. Haslam, York University
Dr. Wenyuan Liao, University of Calgary
Dr. Jiri Patera, Université de Montréal
Dr. Cristian Rios, University of Calgary
Dr. Andrew Toms, York University
Dr. Yuriy Zinchenko, University of Calgary
Funding period: 
July 1, 2021 - March 31, 2021

This project responds to the need for more precise tools to help oil and gas companies better understand where undiscovered energy reserves lie deep within the earth, and to manage and utilize existing reserves. Bringing together mathematicians and geophysicists, this team develops new algorithms to improve upon existing seismic imaging techniques that create accurate images of the earth beneath our feet.

Project Leader(s): 

Dr. Bernard Gendron , Université of Montréal

Project team: 
Dr. Jean-François Cordeau, HEC Montréal
Dr. Sophie D’Amours, Université Laval
Dr. Jacques Ferland, Université de Montréal
Dr. Jean-Marc Frayret, École Polytechnique de Montréal
Dr. Michel Gendreau, Université de Montréal
Dr. Luc Lebel, Université Laval
Dr. Gilles Pesant, HEC Montréal
Dr. Louis-Martin Rousseau, École Polytechnique de Montréal
Non-academic participants: 
Funding period: 
October 1, 2021 - March 31, 2021

The forest industry is an extremely important sector of Canadian economic activity as it represents the largest part of Canada’s trade surplus and 3% of its GDP. Canadian forests sustain an industry that continues to support a significant number of jobs. The worldwide increase in competition and excess production capacity have considerable implications for the situation of the Canadian forest industry and threaten its position in international markets.

Project Leader(s): 

Dr. Jack A. Tuszynski , University of Alberta

Project team: 
Dr. Thoms Hillen, (University of Alberta)
Dr. Gerda de Vries, (University of Alberta)
Dr. Michael Y. Li, (University of Alberta)
Dr. D. Peter Tieleman, (University of Calgary)
Dr. Lukasz Kurgan, (University of Alberta)
Dr. Eric Cytrynbaum, (University of British Columbia)
Dr. Stephane Portet, (University of Manitoba)
Dr. Siv Sivaloganathan, (University of Waterloo)
Dr. Roderick Melnik, (Wilfrid Laurier University)
Funding period: 
July 1, 2021 - March 31, 2021
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