Statistical Machine Learning with Applications in Industrial Processes (in cooperation with Tata Steel)

最近机器学习领域的创新在很大程度上是由大数据的出现推动的，大数据是相关的、高维的、动态的，而且往往不在欧几里得空间内。博士项目旨在开发新的统计机器学习方法和算法，以解决出现的建模挑战。该项目的动机是需要建立预测模型来理解钢铁制造过程。目前，我们正在探索网络科学的可能应用，寻找一种新的包含协变量信息的生成网络模型。

Recent innovations in machine learning has been largely driven by the emergence of Big Data that are correlated, high-dimensional, dynamic and often not in the Euclidean space. The PhD project is aimed at developing novel statistical machine learning methodology andalgorithms to address the modelling challenges that arise. The project is motivated by the need for building predictive models to understand steel manufacturing processes. Currently we are exploring possible applications of network science, looking for a new generative network model that incorporates covariate information.