Optimal Strategies for Antiviral Treatment during an Influenza Epidemic
Postdoctoral fellow: Dr. Majid Jaberi-Douraki, Mathematics and Statistics, York University
Lead faculty member: Dr. Seyed Moghadas, Mathematics and Statistics, York University
A major pharmaceutical intervention for management of many infectious diseases is the use of antiviral drugs. However, the rise of drug resistance poses significant threats to the effectiveness of drugs. This research proposes to determine optimal treatment strategies, through the development of population dynamical models for disease transmission and control, which can minimize the effect of resistance emergence in the population. This work will primarily focus on influenza infection, which still inflicts substantial morbidity, mortality, and socioeconomic costs worldwide.
Statistical Learning of Complex Data with Complex Distributions
Dr. Yoshua Bengio, Université de Montréal
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.
Mathematical Modelling in Pharmaceutical Development
Dr. Jack A. Tuszynski , University of Alberta
Kinetana, Inc.
Biomira, Inc.
Project CyberCell Inc.
Technology Innovations, LLC
National Institute for Nanotechnology
Cross Cancer Institute
McBride Career Group
YeTaDel Foundation
Oncovista Inc.
Howard J. Greenwald P.C.
Multimedia Advanced Computational Infrastructure (MACI)
Canadian-European Research Initiative on Nanostructure (CERION)
