Heidelberg Institute for Theoretical Studies

The Heidelberg Institute for Theoretical Studies (HITS gGmbH) was established in 2010 by SAP co-founder Klaus Tschira through his foundation, the “Klaus Tschira Stiftung”, as a private, non-profit research institute. HITS conducts basic research involving the processing structuring and analysis of large amounts of data in the natural sciences, mathematics and computer science. The research topics range from molecular biology to astrophysics. Shareholders of HITS are the “HITS-Stiftung”, Heidelberg University and the Karlsruhe Institute of Technology (KIT). HITS cooperates with universities and research institutes, as well as with industrial partners. The prime external funding sources are the Federal Ministry of Education and Research, the German Research Foundation and the European Union.

At the moment, HITS comprises following research groups:

The junior group Astroinformatics was founded 2013 at HITS to develop new approaches to analyze and process the increasing amount of data in astronomy. The approaches of this group are based on machine/statistical learning and assist the researchers in performing the required analyses.

The Computational Biology (CBI) group works at the interface between computer science, mathematics and the biological sciences. The research focuses on the computational and algorithmic foundations of genome biology.

The CST group works on mathematical foundations and statistical methodology for forecasting. The aim is to develop methods for probabilistic forecasts, to generate predictive probability distributions for future events and quantities. For example, probabilistic forecasts are used in weather prediction and economics. The group’s second research focus is on spatial statistics, which is concerned with the analysis and interpretation of spatially distributed data.

The research group DMQ makes use of state-of-the-art technology from the fields of High Performance Computing and Uncertainty Quantification in order to quantify uncertainties in large data sets towards reliable insights in Data Mining.

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