Modelling Growth Charts with Measurement Error: A Modern Perspective of Prediction of Abnormal Growth Responses in Young Children and Adults
Postdoctoral fellow: Dr. Taraneh Abarin, Public Health Sciences, University of Toronto
Lead faculty member: Dr. Laurent Briollais, Public Health Sciences, University of Toronto
Using modern statistical measurement error methodologies and analysis, we aim to efficiently and accurately discover and characterize predictive models of responses associated with abnormal growth development in young children and adults. This proposal is unique in scope and vision by addressing health issues that threaten the sustainability of the health care system.
Dr. François Anctil, Université Laval
The goal of this project is to evaluate if mesoscale (35 km) meteorological ensemble forecasts coupled to a short-range hydrological forecasting system can lead to improved forecasts, and thus help maximize hydropower production and minimize flood risks. Positive results would pave the way for a full project which would aim to design an efficient short-range hydrological ensemble forecasting system adapted to the climate and hydrology of the Great-Lakes and Saint Lawrence River basin.
Dr. Changbao Wu, University of Waterloo
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.