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Project Leader(s): 

Postdoctoral fellow: Dr. Nima Safaei, Mechanical and Industrial Engineering, University of Toronto

Lead faculty member: Dr. Andrew K.S. Jardine, Mechanical and Industrial Engineering, University of Toronto

Non-academic participants: 

The research is aimed at providing effective long-term resource planning to effective scheduling of the maintenance tasks over a short-term horizon. The Bombardier Company provides the necessary requirements to the customers around the world to do the predefined maintenance tasks as well as unexpected repair jobs for their aircraft fleet. These services are performed as onsite or offsite, i.e., different centres or stations.

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.

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. Steven Easterbrook, (University of Toronto)

Project team: 
Dr. Marsha Chechik, (University of Toronto)
Dr. Mehrdad Sabetzabeh, (University of Toronto)
Dr. Shiva Nejati, (University of Toronto)
Non-academic participants: 

Bell Canada University Labs, 
IBM Canada for Advanced Studies

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. 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. George Karakostas , (McMaster University)

Project team: 
Dr. Adrian Vetta (McGill University)
Dr. James A. Dimarogonas (MITRE Corporation)
Dr. F. Bruce Shepherd (Bell Laboratories)
Dr. Gordon Wilfong (Bell Laboratories)
Dr. Uyen Trang Nguyen (York University)
Non-academic participants: 
Funding period: 
October 1, 2021 - March 31, 2021

Game Theory studies the phenomena occurring when independent, autonomous entities, called agents or users, act selfishly; game theoretic techniques are now being used to model and analyze networks. This project aims to develop a more realistic modelling of communication and data networks of selfish users using game-theoretic models, study the effects that selfish behaviour has on the overall network performance, and the designs of networks which prevent the rapid degradation of the performance due to such behaviour.

Project Leader(s): 

Dr. Barry Sanders, University of Calgary

Project Website: 
Project team: 
Dr. Andrew Childs, University of Waterloo
Dr. Richard Cleve, University of Waterloo
Dr. Peter Hoyer, University of Calgary
Dr. Michele Mesca, University of Waterloo
Dr. Ashwin Nayak, University of Waterloo
Dr. David Poulin, University of Sherbrooke
Dr. Robert Raussendorf, University of British Columbia
Dr. Ben Reichardt, University of Waterloo
Dr. John Watrous, University of Waterloo
Funding period: 
April 1, 2021 - March 31, 2021

As the size of computer components approaches the atomic scale, quantum technologies will be necessary for the storing and processing of information. The ability to exploit quantum mechanics opens up a whole new mode of computation that may allow computations previously thought infeasible or impossible. Thus, this project team is working to develop novel systems and techniques for information processing, transmission and security by exploiting the properties of quantum mechanical operations.

Project Leader(s): 

[url=mailto:[email protected]]Dr. Laurent Briollais[/url] , University of Toronto

Project team: 
Dr. Gary Bader, University of Toronto
Dr. Adrian Dobra, University of Washington
Dr. Hélène Massam, York University
Dr. Hilmi Ozcelik, Samuel Lunenfeld Research Institute
Non-academic participants: 

[url=]Genizon Biosciences Inc.[/url]

[url=]Wolfram Research[/url]


[url=]Translational Genomics Research Institute[/url]

Funding period: 
1 April 2021 - 31 March 2021

Graphical models have been one of the most efficient statistical tools used in the last twenty years for the analysis of complex structured high-dimensional data. Graphical models provide a probabilistic framework for making inference and representing the knowledge that we have about these complex structured data. In biological research and more particularly in the emerging -omics disciplines such as genomics, proteomics, metabolomics, transcriptomics, data are often generated from complex high throughput experiments and from complex experimental designs.