Diyora Salimova
ETH Zurich

Diyora Salimova
Chair for Mathematical Information Science
Department of Information Technology and Electrical Engineering
ETH Zurich
Sternwartstrasse 7
8092 Zürich

Office: Room E 117
Phone: +41 44 632 26 04

E-mail: sdiyora (add "")

Links: [Profile on ResearchGate] [Profile on GoogleScholar] [Profile on MathSciNet] [Homepage at ETHZ]

Research interests

  • approximation properties of deep neural networks, mathematics for deep learning, machine learning, stochastic gradient descent methods, computational stochastics/stochastic numerics, stochastic differential equations, stochastic analysis, numerical analysis, learning dynamical systems


  • from 06/2020:          Lecturer at ETH Zurich.
  • from 02/2020:          ETH Foundations of Data Science postdoctoral fellow, ETH Zurich. Mentor: Prof. Dr. Helmut Bölcskei
  • 01/2020:                   Postdoc at ETH Zurich, D-MATH, Seminar for Applied Mathematics


  • 09/2016-12/2019:     Doctor of Sciences of ETH Zurich, Switzerland. PhD supervisor: Prof. Dr. Arnulf Jentzen
  • 09/2013-10/2015:     Master of Science in Applied Mathematics, ETH Zurich, Switzerland
  • 09/2011-06/2013:     Bachelor of Science in Mathematics, Jacobs University Bremen, Germany
  • 09/2009-06/2011:     Completed two years of study in undergraduate Mathematics, Samarkand State University, Uzbekistan


(authors listed in alphabetical order)
  • Bercher, A., Gonon, L., Jentzen, A., and Salimova, D., Weak error analysis for stochastic gradient descent optimization algorithms. [arXiv] (2020), 123 pages. Revision requested from Springer Lect. Notes Math.
  • Hornung, F., Jentzen, A., and Salimova, D., Space-time deep neural network approximations for high-dimensional partial differential equations. [arXiv] (2020), 52 pages.
  • Grohs, P., Jentzen, A., and Salimova, D., Deep neural network approximations for Monte Carlo algorithms. [arXiv] (2019), 45 pages. Accepted in Springer Nat. Part. Diff. Equ. Appl.
  • Beccari, M., Hutzenthaler, M., Jentzen, A., Kurniawan, R., Lindner, F., and Salimova, D., Strong and weak divergence of exponential and linear-implicit Euler approximations for stochastic partial differential equations with superlinearly growing nonlinearities. [arXiv] (2019), 65 pages.
  • Jentzen, A., Mazzonetto, S., and Salimova, D., Existence and uniqueness properties for solutions of a class of Banach space valued evolution equations. [arXiv] (2018), 28 pages.
  • Jentzen, A., Salimova, D., and Welti, T., A proof that deep artificial neural networks overcome the curse of dimensionality in the numerical approximation of Kolmogorov partial differential equations with constant diffusion and nonlinear drift coefficients. [arXiv] (2018), 48 pages. Accepted in Comm. Math. Sci.

Published papers

(authors listed in alphabetical order)
  • Mazzonetto, S., and Salimova, D., Existence, uniqueness, and numerical approximations for stochastic Burgers equations. Stoch. Anal. Appl. 38 (2020), no. 4, 623-646. [arXiv].
  • Jentzen, A., Salimova, D., and Welti, T., Strong convergence for explicit space-time discrete numerical approximation methods for stochastic Burgers equations. J. Math. Anal. Appl. 469 (2019), no. 2, 661-704. [arXiv].
  • Hutzenthaler, M., Jentzen, A., and Salimova, D., Strong convergence of full-discrete nonlinearity-truncated accelerated exponential Euler-type approximations for stochastic Kuramoto-Sivashinsky equations. Comm. Math. Sci. 16 (2018), no. 6, 1489-1529. [arXiv].
  • Gerencsér, M., Jentzen, A., and Salimova, D., On stochastic differential equations with arbitrarily slow convergence rates for strong approximation in two space dimensions. Proc. Roy. Soc. London A 473 (2017). [arXiv].
  • Ibragimov, Z. and Salimova, D., On an inequality in l_p(C) involving Basel problem. Elem. Math. 70 (2015), 79-81.