Research field: Machine learning and data science
Digital data and media are ubiquitous today. Digitalization is still in its infancy; it is now beginning to permeate and reshape all aspects of our lives. One of the key challenges of our time is to harness its enormous potential, which has only been tapped to a limited extent so far, and to develop it further in a responsible manner.
Within the framework of this field of research, scientific foundations are being laid—and taught in degree programs—that will help to exploit the potential of digitization. Approaches pursued here include, for example, the urgently needed development of mathematical foundations that allow guarantees of success to be established for machine learning algorithms. These enable the use of novel, extremely powerful machine learning methods in medical and security-related applications.
By linking data sciences with other disciplines, scientifically sound methods, processes, algorithms, and systems for extracting insights, patterns, and conclusions from both structured and unstructured data are made possible. In this way, decision-making processes in human activity can be optimized in a data-driven manner.
Participating disciplines:
Mathematics, computer science, economics, psychology, nursing sciences, philosophy, linguistics and literary studies, journalism, sociology
Participating institutions:
Mathematical Institute for Machine Learning and Data Science
- Contact: Prof. Dr. Götz Pfander (Chair of Mathematics – Scientific Computing), spokesperson for the institute
Seven tenure-track professorships focusing on digitalization in the fields of journalism, primary school education/primary school didactics, Romance linguistics, mathematics, psychology, and economics, established through the Federal-State Program for Young Scientists
- Contact: Prof. Dr. Karin Boczek (tenure-track professorship for digital journalism), spokesperson for the tenure-track professorships
Contact person for the research field:
- Prof. Dr. Jens Hogreve, Vice President for Research and Early Career Researchers