On April 12h, 2023 the Data Science Initiative organized a symposium on Explainable Machine Learning


First Session 14:00 – 15:30

14:00 Opening
14:05 Keynote Holzinger et al (Institute of Forest Engineering)
          Human-Centered AI for smart forest operations: Group Research topics
14:50 Sangeeta Kumari (Core Facility Bioinformatics):
          Network pharmacology: potential target proteins
15:10 Stefan Böhmdorfer (Institute of Chemistry of Renewable Resources):
          Turning chemical information more visible: Visualization and automated classification of chromatographic results

Coffee break 15:30 – 16:00

Second Session 16:00 – 17:30

16:00 Mathew Herrnegger (Institute of Hydrology and Water Management):
          Machine Learning for modeling and prediction of discharge,
          and the examination of the model internals to gain insight into the underlying processes
16:20 Enrico Soranzo (Institute of Geotechnical Engineering):
          Genetic programming for the prediction of the tunnel face support pressure
16:40 Martin Riegler (Wood K plus):
          Machine learning for wood technology research
17:00 Emma Izquierdo-Verdiguier (Institute of Geomatics):
          Feature selection based on random forest for classification and regression tasks in remote sensing
17:20 Discussion

Networking with snacks and drinks 17:30 – open end