Statistics meets Logistics at the interdisciplinary Summer School of the Graduate School of Logistics
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Markus Pauly of the Department of Statistics hosted this year's Graduate School of Logistics Summer School on "Data-Driven Methods for Logistics" in early July. The methodological topics covered ranged from data collection, quality, and quantity to statistical modeling of independent and dependent data to optimization and theory of complex statistical learning techniques. From Markus Pauly's research group, Daniel Horn and Burim Ramosaj (PostDoc until 2022) also gave participants valuable statistical advice for the task at the end of the week. The program was rounded off by presentations on successful Data Science examples and projects from logistics practice before everyone faced the solution of the Live Case Study. In this, interdisciplinary teams were allowed to develop a solution concept for a real Data Science problem over two days.
The Graduate School team rounded off the scientific content with a varied program and tasty meeting opportunities, among other things. More information about the successful teams and other highlights can be found on the Graduate School page.