My primary research includes the characterization, design, and evaluation of visual-interactive interfaces to combine the strengths of both humans and algorithms in interactive machine learning and data science applications. My data-centered focus is on time series data as well as multivariate data. My technique-driven focus ranges from unsupervised to supervised machine learning including cluster analysis, dimensionality reduction, active learning, regression analysis, and classification. From a task perspective most of my work supports exploratory data analysis, i.e., sense-making, decision-making, and hypotheses-building for undiscovered data. Important application domains so far include climate and Earth observation, digital libraries, human motion analysis, service and energy network analysis, political decision-making, music classification, sports data analysis, stock chart analysis, as well as medical and patient-related research in particular.