Vladimir Spokoiny

Valeriy Avanesov, Christian Bayer, Franz Besold, Simon Breneis, Oleg Butkovsky, Pavel Dvurechensky, Vaios Laschos, Jörg Polzehl, John G. M. Schoenmakers, Alexandra Suvorikova, Karsten Tabelow, Nikolas Tapia, Yannic Vargas

Christine Schneider

Honorary Members:
Peter Friz

The research group Stochastic Algorithms and Nonparametric Statistics focuses on two areas of mathematical research, Statistical data analysis and Stochastic modeling, optimization, and algorithms. The projects within the group are related to timely applications mainly in economics, financial engineering, life sciences, and medical imaging. These projects contribute in particular to the main application areas Optimization and control in technology and economy and Quantitative biomedicine of the WIAS.

Specifically, the mathematical research within the group concentrates on the

  • modeling of complex systems using methods from nonparametric statistics,
  • statistical learning,
  • risk assessment,
  • valuation in financial markets using efficient stochastic algorithms and
  • various tools from classical, stochastic, and rough path analysis.

The research group hosts the focus plattform Quantitative analysis of stochastic and rough systems. Furthermore, the group contributes to the development of statistical software, especially in the area of imaging problems in the neurosciences.


  • The new MATH+-Projekt AA4-9 "Volatile electricity markets and battery storage: A model based approach for optimal control" (PIs: Ch. Bayer, D. Kreher (HU Berlin) und M. Landstorfer) was approved to be funded.
  • The new MATH+-project EF3-11 "Quantitative tissue pressure imaging via PDE-informed assimilation of MR data" (PIs: A. Caiazzo, K. Tabelow und I. Sack (Charité Berlin)) was approved to be funded.
  • MATH+-project AA4-2 "Optimal control in energy markets using rough analysis and deep networks" (PIs: Ch. Bayer, P. Friz, J. Schoenmakers and V. Spokoiny) was approved to be funded until March 31, 2025.
  • On August 18, 2021 Darina Dvinskikh defended her PhD thesis with predicate summa cum laude.
  • The article "On a combination of alternating minimization and Nesterov's momentum" by S. Guminov, P. Dvurechensky, N. Tupitsa, and A. Gasnikov was accepted to "International Conference on Machine Learning 2021." (WIAS-Preprint 2695)
  • The article "Newton method over networks is fast up to the statistical precision" by A. Daneshmand, G. Scutari, P. Dvurechensky, and A. Gasnikov) was accepted to "International Conference on Machine Learning 2021."
  • The new MATH+-project EF3-8 "Analysis of brain signals by Bayesian Optimal Transport" (PIs: P. Dvurechensky, K.-R. Müller (TU Berlin), S. Nakajima (TU Berlin), and V. Spokoiny) has been approved for funding.
  • The new MATH+-project EF3-9 "Mathematical Framework for MR Poroelastography" (PIs: A. Caiazzo, K. Tabelow, I. Sack (Charité Berlin)) has been approved for funding.
  • The article "Statistical inference for Bures-Wasserstein barycenters" by A. Kroshnin, V. Spokoiny, A. Suvorikova will appear at "The Annals ofApplied Probability ". (WIAS-Preprint 2788)
  • On May 12, 2020 Kirill Efimov successfully defended his dissertation "Adaptive nonparametric clustering" at Humboldt-Universität zu Berlin (supervisor Vladimir Spokoiny).
  • A paper "Strong existence and uniqueness for stable stochastic differential equations with distributional drift" (authors: Siva Athreya, Oleg Butkovsky, and Leonid Mytnik) appeared in the journal "The Annals of Probability" Volume 48, Number 1 (2020), 178-210.
  • The monograph "Magnetic Resonance Brain Imaging with R" by Jörg Polzehl and Karsten Tabelow was published by Springer.
  • Christian Bayer obtained his habilitation from Technical University Berlin.
  • The Research Unit 2402 Rough paths, stochastic partial differential equeations and related topics was approved to be funded for another period. The research group contributes with the project "Numerical analysis of rough PDEs" (PIs: Christian Bayer, John Schoenmakers)