Computational Harmonic Analysis, Mathematical Data and Image Analysis

The main theme of my mathematical research is Computational Harmonic Analysis with a special focus on Mathematical Data Analysis and Image Processing. 

We are part of the Applied Harmonic Analysis Cluster (AHA).

I am Vice-Director of Studies at the Faculty of Mathematics and I am co-organizer of the bi-annual conference on Applied Harmonic Analysis in Strobl/Austria.


  • M. Ehler, K. Gröchenig, A. Klotz: Quantitative approximation of the Fourier transform on the real line by the discrete Fourier transform, preprint, 2024.

Publications of the last 5 years

  • D. Haider, V. Lostanlen, M. Ehler, P. Balazs: Instabilities in Convnets for Raw Audio, accepted in IEEE Signal Processing Letters, arXiv, 2024.
  • A. Breger, C. Karner, M. Ehler: visClust: A visual clustering algorithm based on orthogonal projections, Pattern Recognition, 148, 2024.
  • M. Ehler, K. Gröchenig: An abstract approach to Marcinkiewicz-Zygmund inequalities for approximation and quadrature in modulation spaces, Mathematics of Computation, 2023.
  • M. Ehler, K. Gröchenig: t-design curves and mobile sampling on the sphere, Forum of Mathematics, Sigma, 11, 2023.
  • V. Lostanlen, D. Haider, H. Han, M. Lagrange, P. Balazs, M. Ehler: Fitting auditory filterbanks with multiresolution neural networks, WASPAA, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2023.
  • D. Haider, M. Ehler, P. Balazs: Convex Geometry of ReLU-layers, Injectivity on the Ball and Local Reconstruction, ICML, Proceedings of the 40th International Conference on Machine Learning, PMLR 202:12339-12350, 2023.
  • J. Dick, M. Ehler, M. Gräf, C. Krattenthaler: Spectral decomposition of discrepancy kernels on the Euclidean ball, the special orthogonal group, and the Grassmannian manifold, (Springer) Constr. Approx., (2023).
  • M. Ehler, U. Etayo, B. Gariboldi, G. Gigante, T. Peter: Asymptotically optimal cubature formulas on manifolds for prefixed weights, (sciencedirect) J. Approx. Theory, 271, 105632, 2021. 
  • M. Ehler, M. Gräf, S. Neumayer, G. Steidl: Curve Based Approximation of Measures on Manifolds by Discrepancy Minimization, (Springer) Found. Comput. Math. 2021 
  • A.Breger, G. Ramos Llorden, et al.: Orthogonal projections for image quality analyses applied to MRI, (Nature) Proceedings in Applied Mathematics and Mechanics, Vol 20, 2021.
  • A. Breger, J. I. Orlando, P. Harar, M. Doerfler, S. Klimscha, C. Grechenig, B. S. Gerendas, U. Schmidt-Erfurth, and M. Ehler: On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problems, (Springer) Journal of Mathematical Imaging and Vision (JMIV)  vol. 62, 376-394 (2020) 
  • J. I. Orlando, B. S. Gerendas, et al., Automated Quantification of Photoreceptor alteration in macular disease using Optical Coherence Tomography and Deep Learning, (Nature) Nature Scientific Reports, 10, 5619 (2020).
  • A. Breger, J. I. Orlando, et al.: An amplified-target loss approach for photoreceptor layer segmentation in pathological OCT scans, (arXiv:1908.00764) Springer Lecture Notes in Computer Science (MICCAI), 2019.
  • M. Ehler, S. Kunis, T. Peter, C. Richter: A Randomized Multivariate Matrix Pencil Method for Superresolution Microscopy, (ETNA) Electronic Transactions on Numerical Analysis vol. 51, (2019), 63-74. 
  • M. Ehler, M. Gräf, C. J. Oates: Optimal Monte Carlo integration on closed manifolds, (SpringerStatistics and Computing vol 29 (2019), no. 6, 1203-1214.