Our mission

ELLIS Munich scientists tackle the great challenges of AI explicability and the development of reliable, intelligent algorithms to advance Machine Learning as a core research field.

Unique expertise and thriving collaborations

ELLIS Munich integrates expertise from the Technical University of Munich, Helmholtz Zentrum München and collaborating partners and will contribute to the ELLIS programs on Computer Vision, Health and Earth & Climate.

Coordinators

Prof. Daniel Cremers
ELLIS Munich Co-director

“My ambition is to improve the ability of machines to analyze and interpret image data with a focus on efficiency and optimality. For this purpose we develop convex optimization methods, partial differential equations, DL, graph theory algorithms and statistical inference and implement them with application specialists from various fields.“

 

Prof. Cremers (ELLIS Fellow) holds the Chair of Computer Vision and Artificial Intelligence; he received five ERC grants, including StG/CoG/AdG, was awarded the Gottfried Wilhelm Leibniz-Preis and was Emmy Noether grantee, amongst others; see here for publications. Daniel’s team works on mathematical image processing and pattern recognition.

Prof. Fabian Theis
ELLIS Munich Co-director

"Major lines of inquiry in my lab are harnessing single cell and multi-scale omics data to predict cell fate and to improve the explainability of ML methods. We collaborate across various disciplines to extend ML capabilities in ways that can be readily adopted by researchers."

 

Prof. Theis (ELLIS Fellow) is the Director of the Institute of Computational Biology and of Helmholtz AI at the Helmholtz Zentrum München; see here for publications. He has received the ERC-StG, the Erwin-Schrödinger award, and the Maier-Leibnitz award, amongst others. Fabian’s work focuses on the development and application of ML and DL methods in genomics and biomedicine, more generally. 

@fabian_theis

 

Prof. Massimo Fornasier
ELLIS Munich Co-director

“Our main avenues of enquiry are the concepts of thin structures, sparsity, and compression as in different forms in variational models and the (sparse and mean-field) controllability of various forms of evolutions (gradient flow, quasi-static, and dynamical game evolutions) with a wide range of applications in multiagent systems and ML.”

 

Prof. Fornasier (ELLIS Member) holds the Chair of Applied and Numerical Analysis at TUM; see here for publications. He received the ERC-StG, Biennal Prize (Società Italiana di Matematica Appl. ed Industr.) and the START Preis (Austrian Science Fund), amongst others. Massimo’s research interests center around nonlinear and numerical analysis.