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Academia

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

Reactive Systems are formal systems that cause events in the physical world, in reaction to a set of monitored inputs. Examples include control systems for aircraft, medical devices, industrial processes, and consumer appliances. In many of these examples, safety (and often security) of the system is of paramount importance. To say anything at all about whether such a system is safe or secure, one has to be able to predict its behavior under the conditions that the system may encounter in use.

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.