Our mission is the development of new tools to extract knowledge from noisy, time-stamped, and high-dimensional data on complex systems across the sciences.
Our research addresses the methodological foundations of data science, big data and machine learning. We apply our methods in the context of empirical software engineering, information systems, and computational social science. Our approach is quantitative, data-driven and interdisciplinary, combining methods from computer science, network science, statistics, and theoretical physics.
Through the development of software tools, we actively engage in the transfer of our research to data analytics practitioners and stakeholders in academia and industry.