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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

The management of transportation and production systems often requires solving a sequence of optimization problems, each problem optimizing the utilization of some resources: equipment, personnel, etc. For instance, transit authorities perform bus scheduling followed by daily and monthly driver scheduling; airlines perform aircraft scheduling followed by crew pairing and monthly crew scheduling; and manufacturing companies address manpower scheduling before production scheduling. Such a sequential approach for management was introduced a long time ago when solutions were computed manually.

Project Leader(s): 

Dr. Anthony Vannelli, University of Guelph & Dr. Miguel F, Anjos, University of Waterloo

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. 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.