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

Bachelor Projects:

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

Seminars

• Large-Scale Time Series Analytics (2021/2022)
• Sustainable Machine Learning on Edge Device Clusters (2020, assistance)

Lectures:

• Guest Lecture about distributed discovery of Order Dependencies for the Data Profiling course (2020/2021)

Master Thesis (supervision and assistance):

• Hyper-Parameter Optimization in Time Series Anomaly Detection (Mats Pörschke, ongoing)
• Time Series Anomaly Detection: An Aircraft Turbine Case Study (Jacopo Roberto Nicosia, 2022)
• A2DB: A Reactive Database for Theta-Joins (Julian Weise, 2020)

• Phillip Wenig, Sebastian Schmidl, Thorsten Papenbrock: TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms. Proceedings of the VLDB Endowment (PVLDB), 2022 (to appear)
  [Project Page] 
• Sebastian Schmidl, Phillip Wenig, Thorsten Papenbrock: Anomaly Detection in Time Series: A Comprehensive Evaluation. Proceedings of the VLDB Endowment (PVLDB), 2022
  [Paper]  [Project Page] 
• Sebastian Schmidl, Thorsten Papenbrock: Efficient Distributed Discovery of Bidirectional Order Dependencies. The VLDB Journal (2022)
  [Paper]  [Poster]  [Project Page] 
• 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] 
• 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]