Vladimir Spokoiny

Valeriy Avanesov, Christian Bayer, Franz Besold, Oleg Butkovsky, Darina Dvinskikh, Pavel Dvurechensky, Peter Mathé, Paolo Pigato, Jörg Polzehl, Martin Redmann, John G. M. Schoenmakers, Massimo Secci, Alexandra Suvorikova, Karsten Tabelow, Nikolas Esteban Tapia Muñoz

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 paper "Optimal Tensor Methods in Smooth Convex and Uniformly Convex Optimization" by Alexander Gasnikov, Pavel Dvurechensky, Eduard Gorbunov, Evgeniya Vorontsova, Daniil Selikhanovych, Cesar A. Uribe was accepted to Conference on Learning Theory 2019.
  • The paper "On the Complexity of Approximating Wasserstein Barycenter" by Alexey Kroshnin, Nazarii Tupitsa, Darina Dvinskikh, Pavel Dvurechensky, Alexander Gasnikov, Cesar Uribe was accepted to International Conference on Machine Learning 2019.
  • The paper "On a Database of Simulated TEM Images for In(Ga)As/GaAs Quantum Dots with Various Shapes" by Thomas Koprucki, Anieza Maltsi, Tore Niermann, Timo Streckenbach, Karsten Tabelow, Jörg Polzehl was accepted for NUSOD 2019 and rated as Top 10 contribution.
  • On March 14, 2019 Benjamin Stemper successfully defended his dissertation "Rough volatility models: Monte Carlo, asymptotics and deep calibration" at Technische Universität Berlin (supervisor Christian Bayer).
  • On February 18, 2019 Larisa Adamyan successfully defended her dissertation "Adaptive weights community detection" at Humboldt-Universität zu Berlin (supervisor Vladimir Spokoiny).
  • The Research Unit 2402 Rough paths, stochastic partial differential equations and related topics has been confirmed to be funded for another period. The research group contributes with the project "Numerical analysis of rough PDEs" (PIs: Christian Bayer, John Schoenmakers).
  • Thomas Koprucki (RG1) and Karsten Tabelow (RG6) receive funding for the MATH+-project EF3-1 "Model-based geometry reconstruction from TEM" running from 01/2019-12/2021. [>>more]
  • The new MATH+-project AA4-2 "Optimal control in energy markets using rough analysis and deep networks" (PIs: Peter Friz, Christian Bayer, John Schoenmakers, Vladimir Spokoiny) has been approved with one PostDoc position at WIAS Berlin and one PhD position at TU Berlin
  • The new MATH+-project EF3-3 "Optimal transport for imaging" (PIs: Pavel Dvurechensky, Michael Hintermüller, and Vladimir Spokoiny) has been approved for funding.
  • Christian Bayer and Peter Friz receive funding for the MATH+-project EF1-5 "On robustness of deep neural networks".