Software Engineer
I am a software engineer and Ph.D. student at the Hasso Plattner Institute in Berlin Area, Germany. My current fields of interest are data engineering as well as distributed, scalable, and reactive systems. In my spare time I read about space flight, astronomy, and music or I do sports, such as playing table tennis and running.
I have a passion for clean code and well designed APIs. I quickly grasp new concepts and I enjoy working on new challenges to further improve my skill set. I can confidentially describe myself as a reliable, flexible, and open-minded team player. One of the many ways I benefit teams is by self-initiative, helpfulness and sincerity.
Write me an email or use LinkedIn to contact me. You can explore my software engineering project portfolio on Github.
Publications
- Anthony Bagnall, Matthew Middlehurst, Germain Forestier, Ali Ismail-Fawaz, Antoine Guillaume, David Guijo-Rubio, Arik Ermshaus, Patrick Schäfer, Thorsten Papenbrock, Phillip Wenig, Sebastian Schmidl: An Introduction to Machine Learning from Time Series. Proceedings of the European Conference on Machine Learning and Data Mining (ECML PKDD), 2024 (to appear).
- Sebastian Schmidl, Naumann Felix, Papenbrock Thorsten: AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data. PVLDB 17:(11), 2024. doi: 10.14778/3681954.3681978 . Download.
- Phillip Wenig, Sebastian Schmidl, Thorsten Papenbrock: Anomaly Detectors for Multivariate Time Series: The Proof of the Pudding is in the Eating. Proceedings of the International Conference on Data Engineering Workshops (ICDEW), 2024. doi: 10.1109/ICDEW61823.2024.00018 Download.
- Marcian Seeger, Sebastian Schmidl, Alexander Vielhauer, Thorsten Papenbrock: DPQL: The Data Profiling Query Language. Proceedings of the conference on Database Systems for Business, Technology, and Web (BTW), 2023. doi: 10.18420/BTW2023-19 . Download.
- Sebastian Schmidl, Phillip Wenig, Thorsten Papenbrock: HYPEX: Hyperparameter Optimization in Time Series Anomaly Detection. Proceedings of the conference on Database Systems for Business, Technology, and Web (BTW), 2023. Gesellschaft für Informatik, Bonn. (p. 461-483). doi: 10.18420/BTW2023-22 . Download.
- Phillip Wenig, Sebastian Schmidl, Thorsten Papenbrock: TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms. PVLDB 12:(15), 2022. doi: 10.14778/3554821.3554873 . Download.
- Sebastian Schmidl, Phillip Wenig, Thorsten Papenbrock: Anomaly Detection in Time Series: A Comprehensive Evaluation. PVLDB 9:(15), 2022. DOI: 10.14778/3538598.3538602 . Download.
- Sebastian Schmidl, Thorsten Papenbrock: Efficient Distributed Discovery of Bidirectional Order Dependencies. The VLDB Journal (2022). DOI: 10.1007/s00778-021-00683-4 . Download.
- Julian Weise, Sebastian Schmidl, Thorsten Papenbrock: Optimized Theta-Join Processing. Proceedings of the Conference on Database Systems for Business, Technology, and Web (BTW), 2021. Gesellschaft für Informatik, Bonn. (p. 59-78). DOI: 10.18420/btw2021-03 . Download.
- Sebastian Schmidl, Frederic Schneider, Thorsten Papenbrock: An Actor Database System for Akka. Proceedings of the conference on Database Systems for Business, Technology, and Web (BTW) - Workshopband, 2019. Gesellschaft für Informatik, Bonn. (p. 225-234). DOI: 10.18420/btw2019-ws-23 . Download.
Unrefereed Publications and Reports
- Schmidl, S. (2020). Efficient Distributed Discovery of Bidirectional Order Dependencies. Master Thesis. Faculty of Digital Engineering, University of Potsdam, Potsdam. Download.
- Schmidl, S. & Waack, J. (2019). Distributed Order Dependency Discovery. Technical Report. Hasso Plattner Institute, Potsdam. Download.
- Schmidl, S. (2019). Self-Healing Microservices with Kubernetes. Technical Report. Hasso Plattner Institute, Potsdam. Download.
- Bock, B., Meissner, A., Schiewe, V., & Schmidl, S. (2019). Optimal Self-Sovereign Identity using Blockchain-Technology. Technical Report. Hasso Plattner Institute, Potsdam.
- Schmidl, S. (2019). How to Repair Data? The HoloClean Framework. Technical Report. Hasso Plattner Institute, Potsdam. Download.
- Kroschewski, J. M., Preuß, A., Schmidl, S., Schöne, F. C., Stebner, A., & Straßenburg, N. H. (2018). Entwicklung eines Deep Learning Ansatzes für Image Captioning. Technical Report. Hasso Plattner Institute, Potsdam.
- Bock, B., Fischer, M., & Schmidl, S. (2018). Brand Personality Prediction based on Big Five User Personality Scores. Technical Report. Hasso Plattner Institute, Potsdam.
- Bock, B., Schmidl, S., & Weisgut, M. (2018). Implementation of Log-Based Security Analysis for Cloud Storage Broker. Technical Report. Hasso Plattner Institute, Potsdam. (Report is subject to restriction note)
- Schmidl, S. (2017). Untersuchung von Advanced Persistent Threats: Welche Hinweise finden sich in sicherheitsrelevanten Log-Daten? Bachelor Thesis. Baden-Württemberg Cooperative State University, Karlsruhe and SAP SE, Walldorf. (Thesis is subject to restriction note)