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.
Forschungsgruppenleitung
Prof. Dr. Thomas Setzer +49 (721) 9654 866 setzer∂fzi de |
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Dr. Alexander Gröschel +49 (721) 9654-804 groeschel∂fzi de |
Wissenschaftliche Mitarbeiter
Florian Knöll +49 (721) 9654 820 knoell∂kit edu |
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Jennifer Schoch Tel.: +49 (721) 9654 856 jennifer schoch∂fzi de |
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Kevin Laubis +49 (721) 9654 864 laubis∂fzi.de |
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Julian Bruns
+49 (721) 9654 846 bruns∂fzi.de |
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Nico Rödder +49 (721) 9654 814 roedder∂fzi.de |
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+49 (721) 9654 809 frankenhauser∂fzi.de |
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+49 (721) 9654 818 haubner∂fzi.de |
Domains
- Telecommunications
- Corporate Financial Controlling
- Elektromobility
- Public Management
Methods
- Statistical Learning
- Combinatorial Optimization
- Data Dimensionality Reduction
- Feature Extraction and Generation
Projects:
- 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)
http://css.iism.kit.edu/26_129.php
- 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)
http://css.iism.kit.edu/26_129.php
- 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)
http://css.iism.kit.edu/26_168.php - 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)
http://css.iism.kit.edu/26_145.php - 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)
http://css.iism.kit.edu/26_153.php