Canada Excellence Research Chair in Data Science for Real-time Decision Making

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The CERC objective is on developing new methodologies to exploit "Big Data" and achieve optimized strategies for the current (and future)· era of decision-making.**That involves multi-thread computing, mobile sensors, cheap data storage and**real-time communication and these ingredients define absolutely new and exciting challenges that strongly urge for new algorithms .**Those challenges will be tackled by a tight cooperation between Mathematical Optimization and Machine Learning. The scenario is centered on data. This is because:**• Data is dynamic, i.e., not anymore to be seen as simply defining the "instance" to be solved. The methodology must be tailored to data changing in real time, and often the algorithms to be developed by Mathematical Optimization need to re-optimize over (slightly) changed conditions of traffic networks, of power grids, of supply-chain systems, etc.**• Data is available but complex, i.e., the amount of information reflects the tight integration between problems traditionally addressed separately. Then, Mathematical" Optimization together with Machine Learning must be able to extract the "right" type and quantity of information leading to the definition of the "right" problems to be solved.**• Data is big, i.e., all algorithms both on the learning and optimization sides must go large scale. This reflects the amount of data involved and the complexity of a decision-making process that looks, as never done before, to a much broader picture of the system to be managed and optimized.**It is expected that using a mixture of mixed-integer linear and nonlinear programming as well as metaheuristics and constraint programming techniques, will solve applications in logistics, supply chain, revenue management, design and operation of energy networks, etc. However, often motivated by applications, the chair research focus will be on the innovative integration of those techniques and on the development of new paradigms exploiting both Machine Learning and Mathematical Optimization for data-driven decision-making.