M.Sc. student in Applied Mathematics for Artificial Intelligence and student researcher in explainable machine learning for scientific applications.https://mich1803.github.io/portfolio/https://mich1803.github.io/portfolio/publications/scholar-analysis-of-foreshocks-and-aftershocks-in-a-microseismic-sequence-in-switzerland-using-explainable-a-2026/https://mich1803.github.io/portfolio/publications/scholar-analysis-of-foreshocks-and-aftershocks-in-a-microseismic-sequence-in-switzerland-using-explainable-a-2026/A CNN distinguishes pre- and post-mainshock seismic traces, while SHAP identifies physically meaningful 30–40 Hz features linked to foreshock activity.Sat, 14 Mar 2026 00:00:00 GMThttps://mich1803.github.io/portfolio/publications/scholar-station-level-and-network-wide-shap-explanation-of-cnn-models-for-seismic-cycle-monitoring-evidence--2026/https://mich1803.github.io/portfolio/publications/scholar-station-level-and-network-wide-shap-explanation-of-cnn-models-for-seismic-cycle-monitoring-evidence--2026/CNNs classify foreshocks and aftershocks with high accuracy, while SHAP reveals that station-specific effects strongly influence model interpretability.Sat, 14 Mar 2026 00:00:00 GMThttps://mich1803.github.io/portfolio/publications/scholar-explainable-machine-learning-for-earthquakes-shap-interpretation-of-cnns-to-distinguish-seismic-spec-2025/https://mich1803.github.io/portfolio/publications/scholar-explainable-machine-learning-for-earthquakes-shap-interpretation-of-cnns-to-distinguish-seismic-spec-2025/A CNN classifies foreshocks and aftershocks with 99.66% accuracy, while SHAP links its predictions to a narrow 30 Hz band and reveals possible fault-healing patterns.Wed, 01 Jan 2025 00:00:00 GMT