Valeriy Avanesov, Christian Bayer, Franz Besold, Michele Coghi, Darina Dvinskikh, Pavel Dvurechensky, Peter Mathé, Paolo Pigato, Jörg Polzehl, Martin Redmann, John G. M. Schoenmakers, Karsten Tabelow
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
- 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".
- Darina Dvinskikh and Pavel Dvurechensky presented their work "Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters" (joint with A. Gasnikov, C. Uribe and A. Nedic) at the 32nd Conference on Neural Information Processing Systems in Montreal, Canada. The paper was accepted as a spotlight presentation, i.e., it was in the top 4% out of 4856 submissions.
- Partial Differential Equations
- Laser Dynamics
- Numerical Mathematics and Scientific Computing
- Nonlinear Optimization and Inverse Problems
- Interacting Random Systems
- Stochastic Algorithms and Nonparametric Statistics
- Thermodynamic Modeling and Analysis of Phase Transitions
- Nonsmooth Variational Problems and Operator Equations