Focuses on fundamental developments in statistical machine learning and data modeling aimed at delivering novel methodologies and algorithms specifically tailored to address the challenges of industrial data. Industrial settings often involve complex systems monitored by multiple different sensors. Unlike conventional predictive models, this area focuses on systems exhibiting multiple fault/failure modes, systems comprised of multiple components with interactive degradation processes, and systems with high levels of data censoring and significant data quality issues. Research in this area involves fundamental statistical developments that can then be packaged into next-generation ML tools used by practitioners.