Head:
Vladimir Spokoiny

Coworkers:
Christian Bayer, Simon Breneis, Oleg Butkovsky, Pavel Dvurechensky, Alexei Kroshnin, Vaios Laschos, Luca Pelizzari, John G. M. Schoenmakers, Alexandra Suvorikova, Karsten Tabelow, Nikolas Tapia

Secretary:
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.


Highlights

  • 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 "On a combination of alternating minimization and Nesterov's momentum" by S. Guminov, P. Dvurechensky, N. Tupitsa, and A. Gasnikov was presented 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 presented to "International Conference on Machine Learning 2021."
  • 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)