Dr. Patrick Paitz

Patrick's research focuses on the development of a generic seismic detector for hazardous mass movements, especially in alpine areas.

With his background in theoretical, observational and fiber-optic seismology, he utilizes conventional and statistical learning algorithms to enable seismic detection and early warning of mass movements from continuous seismic recordings of sparse local sensor networks.

His work is in collaboration with the Swiss Data Science Center (SDSC) in the project DATSSFLOW: DATa Science and Mass Movement Seismology: Towards Next Generation of Debris FLOW Warning.

With the goal of developing an operational generic seismic mass movement detector, Patrick combines a wide range of supervised and unsupervised machine learning algorithms to optimize detection accuracy. With a focus on debris flows, mostly data from the Illgraben torrent in Switzerland are used, but more data is envisioned to be included throughout the project.

In addition to the scientific and engineering aspects of his research, Patrick is invested in Open Research Data and Open Science principles. In collaboration with the SDSC, the project makes use of the Renku platform for reproducible data science (https://renkulab.io/) to ensure reproducible and transparent workflows. This will allow a broader community to apply the results and findings in the future, bringing science closer to the community.

Patrick can be found on twitter here: https://twitter.com/ironicscience

A List of publications and conference contributions can be found on google scholar:

https://scholar.google.ch/citations?user=oyCrJNkAAAAJ&hl=en