Vladimir Spokoiny

Christian Bayer, Simon Breneis, Oleg Butkovsky, Pavel Dvurechensky, Wilfried Kenmoe Nzali, Alexei Kroshnin, Vaios Laschos, László Németh, Luca Pelizzari, John G. M. Schoenmakers, Aurela Shehu, Alexandra Suvorikova, Karsten Tabelow, Nikolas Tapia

Christine Schneider

Honorary Members:
Peter Friz

Dr. Jörg Polzehl (retired)

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.

Within the framework of the Mathematical Research Data Initiative (MaRDI) at WIAS, the research group plays an important role together with the research group "Partial Differential Equations". The assigned subgroup is part of both research groups and creates essential contributions to the development of an infrastructure for mathematical research data within the NFDI.


  • The paper "Optimal stopping with signatures" by CH. Bayer, P. Hager, S. Riedel, J.G.M. Schoenmakers appeared in the journal "The Annals of Applied Probability" Volume 33(1): 238-273 (DOI:10.1214/22-AAP1814 )
  • The new MATH+-project AA4-13 "Equilibria for Distributed Multi-Modal Energy Systems under Uncertainty" (PIs: M. Hintermüller, C. Geiersbach, P. Dvurechensky, A. Kannan (HU Berlin)) was approved to be funded.
  • The article "Decentralized Local Stochastic Extra-Gradient for Variational Inequalities" by A. Beznosikov, P. Dvurechensky, A. Koloskova, V. Samokhin, S. U Stich, and A. Gasnikov has been accepted to "Conference on Neural Information Processing Systems 2022".
  • The article "Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise " by E. Gorbunov, M. Danilova, D. Dobre, P. Dvurechensky, A. Gasnikov, G. Gidel has been accepted to "Conference on Neural Information Processing Systems 2022".
  • The article "The power of first-order smooth optimization for black-box non-smooth problems" by A. Gasnikov, A. Novitskii, V. Novitskii, F. Abdukhakimov, D. Kamzolov, A. Beznosikov, M. Takac, P. Dvurechenskii, and B. Gu was presented at "International Conference on Machine Learning 2022".
  • P. Dvurechensky, B. Schmitzer, and G. Steidl organized a mini-symposium on " Multimarginal Optimal Transport" at the SIAM 2022 Conference on Imaging Science (IS22).
  • Pavel Dvurechensky gave an invited talk "Accelerated Alternating Minimization Methods with Application to Optimal Transport" at the One World Optimization Seminar ( https://owos.univie.ac.at/ ).
  • Within the framework of the Mathematical Research Data Initiative (MaRDI), one position each was acquired in the area of "Statistics and Machine Learning" and "Cooperation with Other Disciplines", respectively.
  • The MATH+ Board awarded Prof. Peter Friz with the "MATH+ Distinguished Fellowship " This includes a research grant and featured research activities by MATH+. Congratulations!
  • The article "Solving optimal stopping problems via randomization and empirical dual optimization" by D. Belomestny, Ch. Bender and J.G.M. Schoenmakers appeared online in "Mathematics of Operations Research" .
  • The new MATH+-project EF1-22 "Bayesian optimization and inference for deep networks" (PIs: V. Spokoiny, C. Schillings (HU Berlin)) was approved to be funded.
  • The article "An accelerated method for derivative-free smooth stochastic convex optimization" by E. Gorbunov, P. Dvurechensky and A. Gasnikov will appear in "SIAM Journal on Optimization".
  • The article "Generalized self-concordant analysis of Frank-Wolfe algorithms" by P. Dvurechensky, K. Safin, S. Shtern, and M. Staudigl will appear in "Mathematical Programming".
  • The new MATH+-project 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 "Approximation of SDEs: A stochastic sewing approach" by O. Butkovsky, K. Dareiotis, M. Gerencser appeared in the journal "Probability theory and related fields" Volume 181.4, 2021: 975-1034. (DOI: https://doi.org/10.1007/s00440-021-01080-2 )
  • The paper "Statistical inference for Bures-Wasserstein barycenters" by A. Kroshnin, V. Spokoiny, A. Suvorikova appeared in the journal "The Annals of Probability" Volume 31(3): 1264-1298. (DOI: 10.1214/20-AAP1618)