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Staff Dr. Tim Jahn

Mr. Jahn has left the institute. This page is no longer maintained.

Contact Information

E-Mail: ed tod nnob-inu tod sni ta nhaja tod b@foo tod de


Winter semester 2021/22

See teaching activities of the whole group.

Current Research Projects



  1. Efficient solution of ill-posed integral equations through averaging. M. Griebel and T. Jahn. Available as INS Preprint No. 2401, 2024. BibTeX PDF
  2. Convergence of generalized cross-validation for an ill-posed integral equation. T. Jahn. Available as INS Preprint No. 2303, 2023. BibTeX PDF
  3. Discretisation-adaptive regularisation of statistical inverse problems. T. Jahn. Universität Bonn, 2022. BibTeX arxiv


  1. Regularizing linear inverse problems under unknown non-gaussian white noise allowing repeated measurements. B. Harrach, T. Jahn, and R. Potthast. IMA Journal of Numerical Analysis, 43(1):443–500, 2023. BibTeX arxiv
  2. Noise level free regularization of general linear inverse problems under unconstrained white noise. T. Jahn. SIAM/ASA Journal on Uncertainty Quantification, 11(2):591–615, 2023. BibTeX arxiv
  3. A probabilistic oracle inequality and quantification of uncertainty of a modified discrepancy principle for statistical inverse problems. T. Jahn. Electronic Transactions on Numerical Analysis, 57:35–56, 2022. BibTeX DOI arxiv
  4. Optimal convergence of the discrepancy principle for polynomially and exponentially ill-posed operators under white noise. T. Jahn. Numerical Functional Analysis and Optimization, 43(2):145–167, 2022. BibTeX DOI arXiv arxiv
  5. A modified discrepancy principle to attain optimal convergence rates under unknown noise. T. Jahn. Inverse Problems, 37(9):095008, 2021. BibTeX DOI arxiv
  6. Beyond the bakushinkii veto: regularising linear inverse problems without knowing the noise distribution. B. Harrach, T. Jahn, and R. Potthast. Numerische Mathematik, 145(3):581–603, 2020. BibTeX DOI arxiv
  7. On the discrepancy principle for stochastic gradient descent. T. Jahn and B. Jin. Inverse Problems, 36(9):095009, 2020. BibTeX DOI arxiv
  8. The sensorimotor loop as a dynamical system: how regular motion primitives may emerge from self-organized limit cycles. B. Sándor, T. Jahn, L. Martin, and C. Gros. Frontiers in Robotics and AI, 2:31, 2015. BibTeX DOI


  1. Regularising linear inverse problems under unknown non-Gaussian noise. T. N. Jahn. PhD thesis, Institute for Mathematics, Johann Wolfgang Goethe-Universität Frankfurt am Main, 2020. BibTeX PDF
  2. The high temperature regime of a multi-species mean field spin glass. T. Jahn. Master's thesis, Insititute for Mathematics, Johann Wolfgang Goethe-Universität Frankfurt am Main, 2016. BibTeX PDF
  3. Limit cycles of neuron controlled robots in simulated physical environment. T. Jahn. Bachelor's Thesis, Institute for Theoretical Physics, Johann Wolfgang Goethe-Universität Frankfurt am Main, 2015. BibTeX PDF