Former Ph.D. student at the Infomation Systems Research Group at Hasso Plattner Institute for Software Systems Engineering

• Distributed Computing
• Machine Learning
• Time Series Analysis

Time Series Anomaly Detection

• DADS (Distributed Detection of Sequential Anomalies in Univariate Time Series)
• Root-Cause Analysis in High-Dimensional Time Series
• Approximation of Hierarchical Clustering for Faster Time Series Analysis

Distributed Computing

• Sending Data instead of Gradients for Distributed Machine Learning with DataGossip
• Distributed Actor System in Rust with Actix-Telepathy
• Distributed Time Series Anomaly Detection

Summer semester 2020

• Sustainable Machine Learning on Edge Device Clusters (PS, Master)

Winter semester 2020 / 2021

• UltraMine: Skalierbare Analyse von Messdatenströmen (Bachelor project)

Summer semester 2021

• UltraMine: Skalierbare Analyse von Messdatenströmen (Bachelor project)

Winter semester 2021 / 2022

• Large-Scale Time Series Analytics (PS, Master)

Winter semester 2022 / 2023

• CAST: Classifying Time Series Anomalies (Master project)

• Actix Telepathy (Rust extension for Actix to allow remote actors)
• Tidewater (A Pipeline Tool for Time Series Anomaly Analysis - Detection, Clustering, Classification)
• Series2Graph++ (Distributable Multivariate Anomaly Detection Algorithm)
• TimeEval (Evaluation tool for time series anomaly detection algorithms)
• DataGossip (PyTorch extension for asynchronous distributed data parallel machine learning)

• Phillip Wenig, Thorsten Papenbrock: Actix-Telepathy. Proceedings of the International Workshop on Reactive and Event-Based Languages and Systems (REBLS), 2023
  [Paper]  [GitHub]  [DOI:10.1145/3623506.3623575]
• Florian Siepe, Phillip Wenig, Thorsten Papenbrock: A Few Models to Rule Them All: Aggregating Machine Learning Models. Proceedings of the LWDA Workshops, 2023
  [Paper]
• 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]
• Phillip Wenig, Thorsten Papenbrock: DataGossip: A Data Exchange Extension for Distributed Machine Learning Algorithms. Proceedings of the International Conference on Extending Database Technology (EDBT), 2022
  [Paper]  [GitHub]  [DOI:10.48786/edbt.2022.24]
• Johannes Schneider, Phillip Wenig, Thorsten Papenbrock: Distributed detection of sequential anomalies in univariate time series. The VLDB Journal (2021)
  [Paper]  [Poster]  [Project Page]  [DOI:10.1007/s00778-021-00657-6]