Kunis, Stefan

Wintersemester 2025/26

• Fourieranalysis
• Mathematik für Anwender I
• Mathematische Software
• Oberseminar Angewandte Analysis

Sommersemester 2025

• Mathematische Software

seit 10/2010 Professor für angewandte und numerische Analysis
10/09–05/16 Leiter der Arbeitsgruppe Fast Algorithms for Biomedical Imaging, Helmholtz München
04/06–09/10 Wissenschaftlicher Mitarbeiter, Akademischer Rat und Juniorprofessor, TU Chemnitz
09/03-08/06 Promotion zu Nonequispaced FFT - Generalisation and Inversion, Universität Lübeck
10/98-08/03 Studium der Informatik mit Nebenfach Medizinische Informatik, Universität Lübeck

Fourier-Analysis, schnelle Algorithmen, bildgebende Verfahren

[53] T. Emmrich and S. Kunis. Real and finite field versions of Chebotarev’s theorem. In: arXiv-Preprint (2025). url: https://arxiv.org/abs/2506.02947.

[52] T. Emmrich, M. Juhnke, and S. Kunis. Sparse graph signals—uncertainty principles and recovery. In: GAMM-Mitt. 48.2 (2025). url: https://doi.org/10.1002/gamm.70002.

[51] I. Kovalyov and S. Kunis. Two-dimensional moment problem and Schur algorithm. In: Integral Equations Operator Theory 97.1 (2025). url: https://doi.org/10.1007/s00020-024-02786-3.

[50] T. Emmrich, M. Juhnke, and S. Kunis. Two subspace methods for frequency sparse graph signals. In: Appl. Comput. Harmon. Anal. 74 (2025). url: https://doi.org/10.1016/j.acha.2024.101711.

[49] P. Catala, M. Hockmann, S. Kunis, and M. Wageringel. Approximation and interpolation of singular measures by trigonometric polynomials. In: Constr. Approx. 60.3 (2024). url: https://doi.org/10.1007/s00365-024-09686-0.

[48] M. Hockmann and S. Kunis. Short communication: weak sparse superresolution is well-conditioned. In: SIAM J. Imaging Sci. 16.1 (2023). url: https://doi.org/10.1137/22M1521353.

[47] P. Catala, M. Hockmann, and S. Kunis. Sparse super resolution and its trigonometric approximation in the p-Wasserstein distance. In: PAMM 22.1 (2023). url: https://doi.org/10.1002/pamm.202200125.

[46] T. Emmrich, M. Juhnke-Kubitzke, and S. Kunis. Sparse signals on hypergraphs. In: PAMM 22.1 (2023). url: https://doi.org/10.1002/pamm.202200171.

[45] M. Hockmann, S. Kunis, and R. Kurre. Computational resolution in single molecule localization - impact of noise level and emitter density. In: Biological Chemistry 404.5 (2023). url: https://doi.org/10.1515/hsz-2022-0301.

[44] H. Schäfer, A. Schuster, S. Kunis, T. Bookholt, J. Hardege, K. Rüwe, and J. Brune. The Readiness of Water Molecules to Split into Hydrogen + Oxygen: A Proposed New Aspect of Water Splitting. In: Advanced Materials 35.30 (2023). url: https://doi.org/10.1002/adma.202300099.

[43] S. Kunis, D. Nagel, and A. Strotmann. Multivariate Vandermonde matrices with separated nodes on the unit circle are stable. In: Appl. Comput. Harmon. Anal. 58 (2022). url: https://doi.org/10.1016/j.acha.2022.01.001.

[42] S. Kunis and D. Nagel. On the condition number of Vandermonde matrices with pairs of nearly-colliding nodes. In: Numer. Algorithms 87.1 (2021). url: https://doi.org/10.1007/s11075-020-00974-x.

[41] S. Kunis and J. Rolfes. Another Hilbert inequality and critically separated interpolation nodes. In: PAMM 21.1 (2021). url: https://doi.org/10.1002/pamm.202100219.

[40] M. Hockmann, S. Kunis, and R. Kurre. Towards a mathematical model for single molecule structured illumination microscopy. In: PAMM 20.1 (2021). url: https://doi.org/10.1002/pamm.202000075.

[39] S. Kunis and D. Nagel. On the smallest singular value of multivariate Vandermonde matrices with clustered nodes. In: Linear Algebra Appl. 604 (2020). url: https://doi.org/10.1016/j.laa.2020.06.003.

[38] S. Kunis, T. Römer, and U. von der Ohe. Learning algebraic decompositions using Prony structures. In: Adv. in Appl. Math. 118 (2020). url: https://doi.org/10.1016/j.aam.2020.102044.

[37] S. Kunis, H. M. Möller, and U. von der Ohe. Prony’s method on the sphere. In: SMAI J. Comput. Math. S5 (2019). url: https://doi.org/10.5802/smai-jcm.53.

[36] M. Ehler, S. Kunis, T. Peter, and C. Richter. A randomized multivariate matrix pencil method for superresolution microscopy. In: Electron. Trans. Numer. Anal. 51 (2019). url: https://doi.org/10.1553/etna_vol51s63.

[35] S. Kunis, H. M. Möller, T. Peter, and U. von der Ohe. Prony’s method under an almost sharp multivariate Ingham inequality. In: J. Fourier Anal. Appl. 24.5 (2018). url: https://doi.org/10.1007/s00041-017-9571-5.

[34] S. Kunis, B. Reichenwallner, and M. Reitzner. Random approximation of convex bodies: monotonicity of the volumes of random tetrahedra. In: Discrete Comput. Geom. 59.1 (2018). url: https://doi.org/10.1007/s00454-017-9914-7.

[33] S. Kunis and I. Melzer. Fast evaluation of real and complex exponential sums. In: Electron. Trans. Numer. Anal. 46 (2017). url: https://etna.ricam.oeaw.ac.at/vol.46.2017/pp23-35.dir/pp23-35.pdf.

[32] S. Kunis, T. Peter, T. Römer, and U. von der Ohe. A multivariate generalization of Prony’s method. In: Linear Algebra Appl. 490 (2016). url: https://doi.org/10.1016/j.laa.2015.10.023.

[31] Y. Dong, T. Görner, and S. Kunis. An algorithm for total variation regularized photoacoustic imaging. In: Adv. Comput. Math. 41.2 (2015). url: https://doi.org/10.1007/s10444-014-9364-1.

[30] L. Kämmerer, S. Kunis, I. Melzer, D. Potts, and T. Volkmer. Computational methods for the Fourier analysis of sparse high-dimensional functions. In: Extraction of quantifiable information from complex systems. Vol. 102. Lect. Notes Comput. Sci. Eng. Springer, Cham, 2014. url: https://doi.org/10.1007/978-3-319-08159-5_17.

[29] F. Filbir, S. Kunis, and R. Seyfried. Effective discretization of direct reconstruction schemes for photoacoustic imaging in spherical geometries. In: SIAM J. Numer. Anal. 52.6 (2014). url: https://doi.org/10.1137/130944898.

[28] T. Görner and S. Kunis. Effective discretization of the two-dimensional wave equation. In: PAMM 14.1 (2014). url: https://doi.org/10.1002/pamm.201410454.

[27] M. Ehler and S. Kunis. Phase retrieval using time and Fourier magnitude measurements. In: Proc. 10th International Conference on Sampling Theory and Applications (SAMPTA). 2013. url: https://eurasip.org/Proceedings/Ext/SampTA2013/papers/p564-ehler.pdf.

[26] S. Heider, S. Kunis, D. Potts, and M. Veit. A sparse Prony FFT. In: Proc. 10th International Conference on Sampling Theory and Applications (SAMPTA). 2013. url: https://eurasip.org/Proceedings/Ext/SampTA2013/papers/p572-potts.pdf.

[25] T. Görner, R. Hielscher, and S. Kunis. Efficient and accurate computation of spherical mean values at scattered center points. In: Inverse Probl. Imaging 6.4 (2012). url: https://doi.org/10.3934/ipi.2012.6.645.

[24] S. Kunis and I. Melzer. A stable and accurate butterfly sparse Fourier transform. In: SIAM J. Numer. Anal. 50.3 (2012). url: https://doi.org/10.1137/110839825.

[23] L. Kämmerer, S. Kunis, and D. Potts. Interpolation lattices for hyperbolic cross trigonometric polynomials. In: J. Complexity 28.1 (2012). url: https://doi.org/10.1016/j.jco.2011.05.002.

[22] S. Kunis and S. Kunis. The nonequispaced FFT on graphics processing units. In: PAMM 12.1 (2012). url: https://doi.org/10.1002/pamm.201210003.

[21] L. Kämmerer and S. Kunis. On the stability of the hyperbolic cross discrete Fourier transform. In: Numer. Math. 117.3 (2011). url: https://doi.org/10.1007/s00211-010-0322-7.

[20] M. Döhler, S. Kunis, and D. Potts. Nonequispaced hyperbolic cross fast Fourier transform. In: SIAM J. Numer. Anal. 47.6 (2010). url: https://doi.org/10.1137/090754947.

[19] J. Keiner, S. Kunis, and D. Potts. Using NFFT 3—a software library for various nonequispaced fast Fourier transforms. In: ACM Trans. Math. Software 36.4 (2009). url: https://doi.org/10.1145/1555386.1555388.

[18] M. Bebendorf and S. Kunis. Recompression techniques for adaptive cross approximation. In: J. Integral Equations Appl. 21.3 (2009). url: https://doi.org/10.1216/JIE-2009-21-3-331.

[17] M. Gräf, S. Kunis, and D. Potts. On the computation of nonnegative quadrature weights on the sphere. In: Appl. Comput. Harmon. Anal. 27.1 (2009). url: https://doi.org/10.1016/j.acha.2008.12.003.

[16] A. Böttcher, S. Kunis, and D. Potts. Probabilistic spherical Marcinkiewicz-Zygmund inequalities. In: J. Approx. Theory 157.2 (2009). url: https://doi.org/10.1016/j.jat.2008.07.006.

[15] S. Kunis. A note on stability results for scattered data interpolation on Euclidean spheres. In: Adv. Comput. Math. 30.4 (2009). url: https://doi.org/10.1007/s10444-008-9069-4.

[14] M. Gräf and S. Kunis. Stability results for scattered data interpolation on the rotation group. In: Electron. Trans. Numer. Anal. 31 (2008). url: https://etna.ricam.oeaw.ac.at/vol.31.2008/pp30-39.dir/pp30-39.pdf.

[13] S. Kunis and H. Rauhut. Random sampling of sparse trigonometric polynomials. II. Orthogonal matching pursuit versus basis pursuit. In: Found. Comput. Math. 8.6 (2008). url: https://doi.org/10.1007/s10208-007-9005-x.

[12] S. Kunis and D. Potts. Time and memory requirements of the nonequispaced FFT. In: Sampl. Theory Signal Image Process. 7.1 (2008). url: https://doi.org/10.1007/BF03549487.

[11] S. Kunis. Nonequispaced fast Fourier transforms without oversampling. In: PAMM 8.1 (2008). url: https://doi.org/10.1002/pamm.200810977.

[10] S. Kunis and D. Potts. Stability results for scattered data interpolation by trigonometric polynomials. In: SIAM J. Sci. Comput. 29.4 (2007). url: https://doi.org/10.1137/060665075.

[9] J. Keiner, S. Kunis, and D. Potts. Efficient reconstruction of functions on the sphere from scattered data. In: J. Fourier Anal. Appl. 13.4 (2007). url: https://doi.org/10.1007/s00041-006-6915-y.

[8] M. Fenn, S. Kunis, and D. Potts. On the computation of the polar FFT. In: Appl. Comput. Harmon. Anal. 22.2 (2007). url: https://doi.org/10.1016/j.acha.2006.05.009.

[7] J. Keiner, S. Kunis, and D. Potts. Efficient reconstruction of functions on the sphere from scattered data. In: PAMM 7.1 (2007). url: https://doi.org/10.1002/pamm.200700924.

[6] T. Knopp, S. Kunis, and D. Potts. A Note on the Iterative MRI Reconstruction from Nonuniform k-Space Data. In: International Journal of Biomedical Imaging 2007.1 (2007). url: https://doi.org/10.1155/2007/24727.

[5] S. Kunis, D. Potts, and G. Steidl. Fast Gauss transforms with complex parameters using NFFTs. In: J. Numer. Math. 14.4 (2006). url: https://doi.org/10.1163/156939506779874626.

[4] J. Keiner, S. Kunis, and D. Potts. Fast summation of radial functions on the sphere. In: Computing 78.1 (2006). url: https://doi.org/10.1007/s00607-006-0169-z.

[3] M. Fenn, S. Kunis, and D. Potts. Fast evaluation of trigonometric polynomials from hyperbolic crosses. In: Numer. Algorithms 41.4 (2006). url: https://doi.org/10.1007/s11075-006-9017-7.

[2] S. Kunis. Nonequispaced FFT. Generalisation and Inversion. Dissertation. Univ. Lübeck, 2006. url: https://zbmath.org/1111.65116.

[1] S. Kunis and D. Potts. Fast spherical Fourier algorithms. In: J. Comput. Appl. Math. 161.1 (2003). url: https://doi.org/10.1016/S0377-0427(03)00546-6.