Professorin Dr. Gitta Kutyniok

Address Ludwig-Maximilians-Universität München (LMU)
Lehrstuhl Mathematische Grundlagen der Künstlichen Intelligenz
Akademiestraße 7
80799 München

Projects

As Applicant
- Current projects
  - Deep-Learning Based Regularization of Inverse Problems (Priority Programmes)
  - Deep learning for non-local partial differential equations (Priority Programmes)
  - Coordination Funds (Priority Programmes)
  - Spiking Neural Networks: Theoretical Foundations, Trustworthiness, and Energy-Efficiency (Priority Programmes)
- Completed projects
  - Geometrische Eigenschaften der Parametermengen von gewichteten Waveletsystemen (Research Fellowships)
  - Wavelet- und Frame-Theorie (Heisenberg Fellowships)
  - Multiscale representation systems for optimally sparse enconding and analysis of geometric features in 3-dimensional signals for both the continuous and digital setting (Research Grants)
  - Numerical and harmonic analysis of problems with anisotropic features, directional representation systems and the solution of transport dominated problems, in particular, for parameter dependent high dimensional versions (Priority Programmes)
  - Discrete-Valued Sparse Signals: Theory, Algorithms, and Applications (Research Grants)
  - Coordination of the DFG-Priority Programm 1798 (Priority Programmes)
  - Coordination Funds (Priority Programmes)
  - Compressed Sensing Algorithms for Structured Massive MIMO -- Phase II: From Massive MIMO to Massive Wireless Networks (Priority Programmes)
As Spokesperson
- Current projects
  - SPP 2298: Theoretical Foundations of Deep Learning (Priority Programmes)
- Completed projects
  - SPP 1798: Compressed Sensing in Information Processing (CoSIP) (Priority Programmes)
As Participating Researcher
- Current projects
  - EXC 2046: MATH+: Berlin Mathematics Research Center (Clusters of Excellence (ExStra))
- Completed projects
  - GRK 2433: Differential Equation- and Data-driven Models in Life Sciences and Fluid Dynamics (DAEDALUS) (Research Training Groups)
As Project Head
- Completed projects
  - Riemannian Manifold Learning via Shearlet Approximation (CRC/Transregios)
  - Shearlet approximation of brittle fracture evolutions (CRC/Transregios)
  - Selfsimilar structures in turbulent flows and the construction of LES closures (Collaborative Research Centres)
  - Digital representations of manifold data (CRC/Transregios)
  - Deep learning for shape reconstruction (CRC/Transregios)

As Prizewinner
- von Kaven-Preis 2007