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.

• Distributed computing
• Scalable and reactive systems
• Data engineering
• Time series analytics

• GitHub
• LinkedIn
• ORCID

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 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):

• 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)

• 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]

Reviewing

• ACM SIGMOD 2023 Availability and Reproducibility
• SoftwareX Journal
• ACM SIGMOD 2022 Availability and Reproducibility