| 2026 | IDA | Fast Model Selection for Interpretable Gaussian Process Models Using Laplace Approximation. | Andreas Besginow, Thomas Pawellek, Jan David Hwel, Christian Beecks, Markus Lange-Hegermann |
| 2024 | DSAA | Discovering Structural Regularities in Time Series via Gaussian Processes. | Jan David Hwel, Christian Beecks |
| 2024 | EDBT | Frequent Component Analysis for Large Time Series Databases with Gaussian Processes. | Jan David Hwel, Christian Beecks |
| 2024 | SISAP | Identifying Propagating Signals with Spatio-Temporal Clustering in Multivariate Time Series. | Jan David Hwel, Georg Stefan Schlake, Kevin Albrechts, Christian Beecks |
| 2023 | DEXA | Gaussian Process Component Mining with the Apriori Algorithm. | Jan David Hwel, Christian Beecks |
| 2022 | DEXA | Analysis of Extracellular Potential Recordings by High-Density Micro-electrode Arrays of Pancreatic Islets. | Jan David Hwel, Anne Gresch, Tim Berger, Martina Dfer, Christian Beecks |
| 2022 | DSAA | Tracing Patterns in Electrophysiological Time Series Data. | Jan David Hwel, Anne Gresch, Fabian Berns, Ruben Koch, Martina Dfer, Christian Beecks |
| 2022 | ICDE | Evaluating the Lottery Ticket Hypothesis to Sparsify Neural Networks for Time Series Classification. | Georg Stefan Schlake, Jan David Hwel, Fabian Berns, Christian Beecks |
| 2022 | IIWAS | A Comparative Performance Analysis of Fast K-Means Clustering Algorithms. | Christian Beecks, Fabian Berns, Jan David Hwel, Andrea Linxen, Georg Stefan Schlake, Tim Dsterhus |
| 2022 | KI | Dynamically Self-adjusting Gaussian Processes for Data Stream Modelling. | Jan David Hwel, Florian Haselbeck, Dominik G. Grimm, Christian Beecks |
| 2021 | ICDM | LOGIC: Probabilistic Machine Learning for Time Series Classification. | Fabian Berns, Jan David Hwel, Christian Beecks |