Applications of Advanced Statistics and Machine Learning Methods in Nuclear Physics

In this talk we will explore the role that machine learning and advanced statistics play in the theory-experiment/observation cycle of nuclear science. These multidisciplinary methods and techniques have become instrumental for several developments in the field over the last decade and are expected to become indispensable to fully capitalize on the investments in recent and upcoming computational, experimental, and observational facilities. We will discuss key highlights and examples including deep learning for data analysis and control, dimensionality reduction for model acceleration and discovery, and Bayesian machine learning for uncertainty quantification and experimental design. This overview will serve as an introduction for the cutting-edge talks that will follow in the mini symposium.