Cluster of Excellence – Machine Learning for Science

15.03.2021

New Mini-Graduate Schools within the Cluster‘s Innovation Fund program

In the third round of calls for proposals within the framework of our intramural Innovation Fund, so-called "Mini-Graduate Schools" are being funded for the first time - 5 of them were successful, as well as one individual project.

In order to promote the exchange between different scientific disciplines and the implementation of innovative, high-risk projects, members of the Cluster can apply for competitive, institutional Cluster funding ("Innovation Fund").

While in the first two rounds of calls projects involving collaboration between two research groups covering different fields or disciplines were funded, the focus in the 2020 call was on the establishment of Mini-Graduate Schools, consisting of several subprojects. The idea is that several research groups work together on a larger research question relevant to the Cluster and thus generate a visible output.

This year, 5 MiniGrad Schools with 4 subprojects each and one individual project were successful and will now receive funding for 3 years:
  • MiniGrad School "Compositionality in Minds and Machines"
    Zeynep Akata, Matthias Bethge, Martin Butz, Peter Dayan, Enkelejda Kasneci, Eric Schulz, Felix Wichmann, Charley Wu
  • MiniGrad School „Machine Learning in Education“
    Álvaro Tejero-Cantero, Benjamin Nagengast, Bob Williamson, Charley Wu, Detmar Meurers, Enkelejda Kasneci, Konstantin Genin, Kou Murayama, Thomas Grote, Ulf Brefeld, Ulrich Trautwein
  • MiniGrad School „Modeling and Understanding Spatiotemporal Environmental Interactions - MUSTEIN“
    Martin Butz, Volker Franz, Bedartha Goswami, Hendrik Lensch, Nicole Ludwig, Georg Martius, Thomas Scholten, Carl-Johann Simon-Gabriel, Christiane Zarfl
  • MiniGrad School „Probabilistic Inference in Mechanistic Models - PIMMS“
    Philipp Berens, Manfred Claassen, Reinhard Drews, Todd Ehlers, Jakob Macke
  • MiniGrad School „Uncovering the inner structure of medical images through generative modeling“
    Christian Baumgartner, Philipp Berens, Sergios Gatidis, Matthias Hein, Hendrik Lensch, Jakob Macke, Bernhard Schölkopf
  • Individual Project „Inductive bias of learning algorithms in climate science“
    Bedartha Goswami, Ulrike von Luxburg
 Detailed information on the projects can be found on our Cluster page
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