Veranstaltungen

The annual members assembly recapitulates on the events organized by KIT Center MathSEE along with the members and assembles one a year to execute all other formal tasks according to MathSEE foundational documents.

Nicola Gnecco's research aims to develop statistical and machine-learning methods for reliable learning under changing conditions, drawing on causal inference and extreme value theory to improve robustness and understand the limits of extrapolation.

The BAYST workshop aims to bridge recent advances in Bayesian structural learning with applied sciences, fostering scientific discussion and collaboration between theoretical and applied researchers. 
 
BAYST will consist of 20 invited talks (with discussion) and 2 poster sessions (submissions are open!) over two and a half days. Registration is free for all attendees. Coffee breaks, a welcome reception, and lunches will be provided for presenters (of either a poster or a talk).
 
Moreover, BAYST will serve as the kick-off meeting for a potential new section in the International Society of Bayesian Analysis (ISBA) that will be officially proposed to the ISBA Executive Committee after the workshop. The section on Bayesian Structural Learning (BSL) aims to provide an inclusive home for researchers working with high-dimensional or structurally complex data, whether their focus is methodological or applied. Initial officers would be David Dunson, Nadja Klein, Rodney Sparapani, and Deborah Sulem. If you are interested in supporting this initiative, contact us at kleinlab@scc.kit.edu  to sign our petition.