Prof. Dr. Alexander Schiendorfer
Possible applications of combinatorial optimization and machine learning in production & logistics, especially with the requirements of the automotive industry (assembly-intensive, variant-rich production, complex supply chains)
- Constraint programming, mathematical optimization (e.g. MiniZinc, Google OR-Tools, Gurobi, CPLEX)
- Machine learning (graph neural networks, uncertainty models, time series)
Ongoing research projects:
- Since March 2021 Research professor für AI-based Optimization in Automotive Production, Technische
- 2018-2020 Senior researcher (untenured), Institut für Software & Systems Engineering,, University of
- 2013-2018 Research associate and doctorate in computer science, Institut für Software & Systems Engineering,, University of Augsburg
- 2011-2013 M.Sc. Software Engineering (University of Augsburg, TUM, LMU - Elite graduate program)
- 2011 Research internship at Siemens Corporate Research (Princeton, USA)
- 2011 B.Sc. Software Engineering (Hagenberg, Austria)
Full CV available upon request
( https://www.linkedin.com/in/alexander-schiendorfer/ or https://twitter.com/schienal )
- K Dachtler, M Ortner, M Ferri, C Eberst, A Schiendorfer. Data-centric and Goal-oriented AI for Robotic Repair Tasks (preprint), 56th International Symposium on Robotics, September 26-27, 2023
- S Stieber, N Schröter, E Fauster, M Bender, A Schiendorfer, W Reif. Inferring material properties from FRP processes via Sim-to-Real learning (preprint), Journal of Advanced Manufacturing Technology 2023
- C Lenzen, A Schiendorfer, W Reif. Graph machine learning for assembly modeling. In LoG 2022: The First Learning on Graphs Conference, 9-12 December 2022, virtual event.
- C Lenzen (Gajek), A Schiendorfer, W Reif, A Recommendation System for CAD Assembly Modeling based on Graph Neural Networks, (ECML-PKDD 2022)
- L Lodes, A Schiendorfer, A Deep Learning Bootcamp for Engineering & Management Students (TeachML @ ECML-PKDD 2022)
- L Lodes, A Schiendorfer, Certainty Groups: A practical approach to distinguish confidence levels in neural networks, PHM Society European Conference 7 (1), 294-305 (PHM 2022)
- S Bhavnani, A Schiendorfer, Towards copeland optimization in combinatorial problems, International Conference on Integration of Constraint Programming (CPAIOR 2022)
- S Stieber, N Schröter, E Fauster, A Schiendorfer, W Reif, PermeabilityNets: Comparing Neural Network Architectures on a Sequence-to-Instance Task in CFRP Manufacturing, (ICMLA 2021)
- S Böhm, M Neumayer, O Kramer, A Schiendorfer, A Knoll, Comparing Heuristics, Constraint Optimization, and Reinforcement Learning for an Industrial 2D Packing Problem,
- J Wilfert, N Paprotta, O Kosak, S Stieber, A Schiendorfer, W Reif, A real-word realization of the AntNet routing algorithm with ActivityBots (ACSOS-C 2021)
- S. Stieber, N. Schröter, A. Schiendorfer, A. Hoffmann and W. Reif. 2020. FlowFrontNet: improving carbon composite manufacturing with CNNs. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML PKDD 2020, 14-18 September 2020.
- A. Schiendorfer, C. Lenzen (Gajek) and W. Reif. Turning software engineers into machine learning engineers. In Proceedings of the Teaching Machine Learning Workshop at ECML-PKDD 2020, 14 September 2020. ML Research Press
- A. Schiendorfer and W. Reif. “Reducing Bias in Preference Aggregation for Multiagent Soft Constraint Problems”. In: Proc. 25th Intl. Conf. “Principles and Practice of Constraint
Programming” (CP 2019). Springer. 2019, pp. 510–526.
- A. Schiendorfer, A. Knapp, G. Anders, and W. Reif. “MiniBrass: Soft constraints for MiniZinc”.
In: Constraints (July 2018), pp. 403–450. url: https://doi.org/10.1007/s10601-018-9289-2
- G. Anders, A. Schiendorfer, F. Siefert, J.-P. Steghöfer, and W. Reif. “Cooperative Resource
Allocation in Open Systems of Systems”. In: ACM Transactions on Autonomous and Adaptive
Systems (June 2015), 11:1–11:44.