The Canada Excellence Research Chair in Data Science for Real-Time Decision-Making aims at developing new tools and methodologies that will allow enormous volumes of data from multiple sources to be processed and analyzed in real time — in order to obtain usable knowledge and to automate decision-making. By combining processes for analyzing highly targeted data and real-time decision-making, their mathematical model-based tools will help organizations improve performance, by creating highly customized outputs and taking into account the environments, needs and individual behaviors of their clients or users. The applications that result will foster new business models that are based on accurate depictions of user behaviors and expectations, combined with competitors’ responses. The many sectors that could benefit include transportation management, energy, health care and manufacturing, as well as supply chain management and logistics.
The Chair will make it possible to train the next generation of data science specialists, who will be able to master the scientific, technological and economic issues emerging from the big-data explosion. Their multidisciplinary skills and their understanding of business issues will make them a labor force highly sought-after by employers from every sector of the economy who wish to transform their decision-making processes.
Ecole stands for Extensible Combinatorial Optimization Learning Environments and aims to expose a number of control problems arising in combinatorial optimization solvers as Markov Decision Processes. Rather than trying to predict solutions to combinatorial optimization problems directly, the philosophy behind Ecole is to work in cooperation with the state-of-the-art Mixed Integer Linear Programming solver SCIP that acts as a controllable algorithm.
The Chair relies on various public and private partners in hospitals and industrial sectors to play an active role in technological, economic and social development.