As Canada Excellence Research Chair in Data Science for Real-Time Decision-Making at Polytechnique Montréal, Dr. Andrea Lodi holds Canada’s main chair in Operations Research. The Chair’s mission is to combine knowledge acquisition through Machine Learning with decision making through Mathematical Optimization in a unified approach that is able to take advantage of the virtually unlimited quantity of data and lead to Data-Driven Innovation.
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.
Among the various research topics, the CERC in Data Science for Real-Time Decision-Making works on novel approaches classified by the following themes :
Optimization under uncertainty
Integration of Operations Research and Machine Learning