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

CERC的目标是开发新的方法来利用“大数据”，并为当前（和未来）的决策时代实现优化战略。这涉及到多线程计算、移动传感器、廉价数据存储和实时通信，这些因素定义了全新的、令人兴奋的挑战，强烈要求开发新的算法。这些挑战将通过数学优化和机器学习之间的紧密合作来解决。该场景以数据为中心。这是因为：•数据是动态的，也就是说，不再被视为简单地定义要解决的“实例”。该方法必须根据实时变化的数据进行调整，并且通常由数学优化开发的算法需要在交通网络、电网、供应链系统等（略微）变化的条件下重新优化。•数据是可用的，但很复杂，即信息量反映了传统上单独解决的问题之间的紧密集成。然后，数学优化与机器学习必须能够提取“正确”类型和数量的信息，从而定义要解决的“正确”问题。•数据量很大，也就是说，所有学习和优化方面的算法都必须大规模。这反映了所涉及的数据量和决策过程的复杂性，这是前所未有的，需要对需要管理和优化的系统进行更广泛的了解。预计混合整数线性和非线性规划以及元启发式和约束规划技术的结合将解决物流、供应链、收益管理、能源网络设计和运行等方面的应用。然而，通常受到应用程序的激励，主席的研究重点将放在这些技术的创新集成以及利用机器学习和数学优化进行数据驱动决策的新范式的开发上。

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 andreal-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.