Home | deutsch  | Legals | Sitemap | KIT

Analytics & Operations Management

The research group “Analytics and Operations Management” dedicates its work to research and education on data science, predictive analytics, managerial decision making, as well as on the foundations of dimensionality reduction and probabilistic reasoning in large datasets.

Head of Division

Prof. Dr. Thomas Setzer Prof. Dr. Thomas Setzer
+49 (721) 9654 866
setzer∂fzi de

Research Assistants

Sebastian Blanc Sebastian Blanc
+49 (721) 9654 880
sebastian blanc∂kit edu

Florian Knöll Florian Knöll
+49 (721) 9654 820
knoell∂kit edu

Kateryna Shapovlova
Kateryna Shapovlova
+49 (721) 9654 854
kateryna shapovalova∂fzi de
Jennifer Schoch Jennifer Schoch
Tel.: +49 (721) 9654 856
jennifer schoch∂fzi de

Kevin Laubis Kevin Laubis
+49 (721) 9654 864

Julian Bruns Julian Bruns
+49 (721) 9654 846

Julian Bruns Nico Rödder
+49 (721) 9654 814


  • Telecommunications
  • Corporate Financial Controlling
  • Elektromobility
  • Public Management


  • Statistical Learning
  • Combinatorial Optimization
  • Data Dimensionality Reduction
  • Feature Extraction and Generation


  • Accuracy improvement of vast amounts of heterogeneous judgmental cash flow forecasts using analytical debiasing methods and combination with model forecasts (in collaboration with Bayer AG)
  • Concise representation of cash flow forecasting- and revisioning-behavior processing analytical-orthogonal and Bayes-based metrics in corporate financial controlling (in collaboration with Bayer AG)
  • Modelling and prediction of user behavior related electric vehicle high-voltage battery aging, based on heterogeneous field-data (in collaboration with a large German OEM)
  • Design of robust and concise metrics to represent and cluster the purchasing and usage history of telecommunication customers used in campaign management (in collaboration with a global telecommunications company)
  • Development of novel analytical approaches in the context of Geographic Information Systems (GIS) that allow a faster and more reliable consideration of vast amounts of heterogeneous and unreliable data in disaster and emergency management (BMBF-founded Project; program: Big Data) http://css.iism.kit.edu/26_144.php
  • Techniques to decompose constraint matrices in packing problems and step-wise generation of variance-preserving, pseudo-perpendicular constraints aimed at transforming high-dimensional MIP into lower-dimensional problem representations that allow for more efficient and scalable problem solving (in cooperation with Siemens AG)