M.Sc. Lena Schmid

• Kirchhof, M., Schmid, L., Reining, C., Ten Hompel, M., & Pauly, M. (2021a). Chances of interpretable transfer learning for human activity recognition in warehousing. In M. Mes, E. Lalla-Ruiz, & S. Voß (Eds.), Computational logistics (Publisher’s Version, 1st ed., Vol. 13004, pp. 163–177). Springer. https://doi.org/10.1007/978-3-030-87672-2_11
• Kirchhof, M., Schmid, L., Reining, C., Ten Hompel, M., & Pauly, M. (2021b). pRSL: Interpretable multi-label stacking by learning probabilistic rules. In C. de Campos & M. H. Maathuis (Eds.), 37th Conference on Uncertainty in Artificial Intelligence (UAI 2021) (Publisher’s Version, Vol. 161, pp. 461–470). Curran Assoc. Inc. https://proceedings.mlr.press/v161/kirchhof21a.html

•     Wahrscheinlichkeitstheorie (WS 2020/21)
•     Six-Sigma-Methode (SS 2020)
•     Statistische Verfahren (WS 2019/20)
•     Fallstudien I (WS 2019/20)
•     Seminar "Deep Learning in Logistics" (SS 2019)
•     Statistische Verfahren im Qualitätsmanagement (SS 2019)

Wissenschaftliche Tätigkeit

• Seit Apr. 2019: Wissenschaftliche Mitarbeiterin an der Fakultät Statistik

Studium

• M.Sc. (Mathematik): Universität Ulm, 2018. Masterarbeit: Consistency of Random Forests with Application to Facebook Data
• B.Sc. (Mathematik): Universität Ulm, 2016. Bachelorarbeit: Modelling Backbones of Shortest Path Trees in Iterated Random Tessellations.