Recent rapid technological developments have enabled the collection of high-dimensional complex data in engineering, finance, biology, health, and economics, to name a few. Analysis of high-dimensional data is a challenging task, and it is crucial in making evidence-based optimal decisions. Applied probability and optimisation methods play a significant role in data science, making these decisions possible. The goal of this session is to provide a platform for academics and practitioners to present high-quality results related to applied probability and optimisation methods with potential applications in data science.
Key topics: Feature selection, Optimisation methods, Regularisation, High-dimensional data