Head:
Vladimir Spokoiny

Coworkers:
Valeriy Avanesov, Christian Bayer, Nazar Buzun, Fabian Dickmann, Pavel Dvurechensky, Andzhey Koziuk, Peter Mathé, Mario Maurelli, Hans-Joachim Mucha, Paolo Pigato, Jörg Polzehl, Martin Redmann, John G. M. Schoenmakers, Benjamin Stemper, Alexandra Suvorikova, Karsten Tabelow

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 contributes to the development of statistical software, especially in the area of imaging problems in the neurosciences.

Highlights

  • Prof. Peter K. Friz (TU Berlin/WIAS) received the ERC Consolidator Grant Geometric aspects in pathwise stochastic analysis and related topics running from 2016 to 2021. [>>more].
  • Dr. Th. Koprucki (RG1) and Dr. K. Tabelow (RG6) receive funding for the ECMath-project OT7 "Model-based geometry reconstruction of quantum dots from TEM" running from 06/2017-12/2018.
  • Dr. J. Schoenmakers and Prof. Dr. Spokoiny receive funding for the ECMath-project "Decisions in energy markets via deep learning and optimal control" running from 06/2017-12/2018.
  • New publication: J. Polzehl, K. Tabelow, Low SNR in diffusion MRI models, J. Amer. Statist. Assoc., 111 (2016) pp. 1480-1490, DOI 10.1080/01621459.2016.1222284.