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策略。这涉及多线程计算，移动的传感器，廉价的数据存储和 ** 实时通信，这些成分定义了绝对新的和令人兴奋的挑战，强烈要求新的算法。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。场景以数据为中心。这是因为：**·数据是动态的，即，不再被视为简单地定义要解决的“实例”。该方法必须针对真实的时间变化的数据进行调整，并且数学优化开发的算法通常需要在交通网络、电网、供应链系统等的（轻微）变化条件下重新优化。数据可用但复杂，即，信息量反映了传统上分别处理的问题之间的紧密结合。然后，数学优化与机器学习必须能够提取“正确”的信息类型和数量，从而定义要解决的“正确”问题。数据是大的，即，学习和优化方面的所有算法都必须大规模进行。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，要管理和优化的系统的更广泛的画面。预计使用混合整数线性和非线性规划以及元分析和约束规划技术的混合，将解决物流，供应链，收入管理，能源网络设计和运营等方面的应用。主席的研究重点将是这些技术的创新整合和利用机器学习的新范式的发展，和数学优化用于数据驱动的决策。

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.