• EN

Sebastian Schmidl

Ph.D. student at the chair for Infomation Systems at Hasso Plattner Institute for Digital Engineering. I am in the distributed computing research group, where we investigate computationally complex problems and how they can be solved in distributed environments.

 

Contact Information

Hasso-Plattner-Institut für Digital Engineering gGmbH
Prof.-Dr.-Helmert-Str. 2-3
D-14482 Potsdam

Office: F-2.04

Phone: +49 331 5509 4977
Email:

Research Interests

  • Scalable and reactive systems, especially using actor programming
  • Time series anomaly detection
  • Distributed computing
  • Data profiling

Links

Teaching

Master Projects:

  • CAST: Classifying Time Series Anomalies (2022/2023)

Bachelor Projects:

  • UltraMine - Scalable Analytics on Time Series Data (2020/2021)

Seminars:

  • Advanced Data Profiling (Master, 2023/2024)
  • Large-Scale Time Series Analytics (Master, 2021/2022)
  • Sustainable Machine Learning on Edge Device Clusters (Master, 2020, assistance)

Lectures:

  • Guest Lecture about Reproducibility for the Lecture Series on Research Methods (2024/2025)
  • Exercises for the Data Integration course (2024)
  • Guest Lecture about Order Dependencies for the Data Profiling course (2023)
  • Guest Lecture about distributed discovery of Order Dependencies for the Data Profiling course (2020/2021)

Master Thesis (supervision):

  • Detection of Subsequence Anomalies in Univariate Time Series with Convolutional Kernels (Stefan Spangenberg, 2025)
  • DPQLEngine: Processing the Data Profiling Query Language (Marcian Seeger UMR, 2023, supervision assistance)
  • Correlation Anomaly Detection in High-Dimensional Time Series (Niklas Köhnecke, 2023)
  • HYPEX: Explainable Hyperparameter Optimization in Time Series Anomaly Detection (Mats Pörschke, 2022)
  • Time Series Anomaly Detection: An Aircraft Turbine Case Study (Jacopo Roberto Nicosia, 2022)
  • A2DB: A Reactive Database for Theta-Joins (Julian Weise, 2020, supervision assistance)

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
    [Paper]  [vldb]  [Project Page]  [DOI:10.14778/3681954.3681978]
  • 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
    [Paper]  [DOI:10.1109/ICDEW61823.2024.00018]
  • 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
    [Paper]  [DOI:10.18420/BTW2023-19]
  • 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
    [Paper]  [Project Page]  [DOI:10.18420/BTW2023-22]
  • Phillip Wenig, Sebastian Schmidl, Thorsten Papenbrock: TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms. PVLDB 12:(15), 2022
    [Paper]  [Project Page]  [DOI:10.14778/3554821.3554873]
  • Sebastian Schmidl, Phillip Wenig, Thorsten Papenbrock: Anomaly Detection in Time Series: A Comprehensive Evaluation. PVLDB 9:(15), 2022
    [Paper]  [Poster]  [Project Page]  [DOI:10.14778/3538598.3538602]
  • Sebastian Schmidl, Thorsten Papenbrock: Efficient Distributed Discovery of Bidirectional Order Dependencies. The VLDB Journal (2022)
    [Paper]  [Poster]  [Project Page]  [DOI:10.1007/s00778-021-00683-4]
  • Julian Weise, Sebastian Schmidl, Thorsten Papenbrock: Optimized Theta-Join Processing. Proceedings of the Conference on Database Systems for Business, Technology, and Web (BTW), 2021
    [Paper]  [Project Page]  [DOI:10.18420/btw2021-03]
  • 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
    [Paper]  [DOI:10.18420/btw2019-ws-23]

Academic Activity

Reviewing