Université de Montréal

This project is split into two parts. The first is to model the arrival process to take into account the forms of dependence in the forecast call center of Hydro-Quebec. A second aspect concerns the proposal for a control system for real-time reallocation of agents. For the first part the need for more accurate models of stochastic processes has been made by officials of Hydro-Quebec. We therefore propose a model incorporating both dependence and intraday dependence between call types. It would then provide better predictions of future arrival of calls during the day.

Pharmacometrics (PM) is an emerging research area defined as “the science that interprets and describes pharmacology in a quantitative fashion to aid efficient drug development and/or regulatory decision”. Over the years, Dr. Nekka’s team has encompassed deep thinking on how to join and enhance emerging worldwide efforts to make mathematical modeling and simulation a complementary language to the usual empirical and clinical methods used in drug discovery and development. This work is part of these sustained efforts to deal with the complex relationship of dose-exposure-effect of drugs.

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.

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.

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.

The forest industry is an extremely important sector of Canadian economic activity as it represents the largest part of Canada’s trade surplus and 3% of its GDP. Canadian forests sustain an industry that continues to support a significant number of jobs. The worldwide increase in competition and excess production capacity have considerable implications for the situation of the Canadian forest industry and threaten its position in international markets.

The development of new biomedical imaging techniques has resulted in significantly better tools for doctors and scientists to image humans and animals in-vivo. Technological developments and new types of imagers with more capabilities are revolutionizing the field. Currently, available technologies for brain imaging include Magnetic Resonance Imaging (MRI), functional MRI, Diffuse Optical Tomography (DOT), Electro-Encephalography (EEG) and Magneto-Encephalography.