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Association of Commonwealth Universities

Project Leader(s): 

Dr. Michael Monagan, Simon Fraser University & Dr. George Labahn, University of Waterloo

Project team: 
Dr. Jonathan Borwein, Dalhousie University
Dr. Peter Borwein, Simon Fraser University
Dr. Petr Lisonek, Simon Fraser University
Dr. Marni Mishna, Simon Fraser University
Dr. Mark Giesbrecht, University of Waterloo
Dr. Arne Storjohann, University of Waterloo
Dr. Rob Corless, University of Western Ontario
Dr. David Jeffrey, University of Western Ontario
Dr. Marc Moreno Maza, University of Western Ontario
Dr. Greg Reid, University of Western Ontario
Dr. Eric Schost, University of Western Ontario
Dr. Stephen Watt, University of Western Ontario
Dr. Jacques Carette, McMaster University
Dr. Howard Cheng, University of Lethbridge
Dr. Wayne Eberly, University of Calgary
Non-academic participants: 
Funding period: 
February 25, 2022 - March 31, 2021

Computer algebra systems such as Maple compute using mathematical formulae as well as numbers, mechanizing the mathematics used in education and research labs. This project focuses on the design and implementation of algorithms for these systems. Emphasis is placed on efficiency that allows large and complex problems of the type encountered in industrial settings to be solved. In the past year the team has made major advances in the core tools that are needed to solve these complex problems.

Project Leader(s): 

Dr. Guy Lapalme, Université de Montréal

Project team: 
Dr. Philippe Langlais, Université de Montréal
Dr. Pascal Vincent, Université de Montréal
Fabrizio Gotti, Université de Montréal
Non-academic participants: 
Funding period: 
April 1, 2021 - March 31, 2021

This project will explore new ways of customizing and translating the mass of daily information produced by Environment Canada (EC). This information in digital format is later transformed into weather and environmental forecasts, warnings and alerts that must be broadcast in real-time in at least two languages, in many different formats and in a way that takes location into account.

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.