Valeriy Avanesov, Christian Bayer, Franz Besold, Oleg Butkovsky, Darina Dvinskikh, Pavel Dvurechensky, Peter Mathé, Jörg Polzehl, Sebastian Riedel, John G. M. Schoenmakers, Alexandra Suvorikova, Karsten Tabelow, Nikolas Tapia
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 monograph "Magnetic Resonance Brain Imaging with R" by Jörg Polezhl and Karsten Tabelow was published by Springer.
- Christian Bayer obtained his habilitation from Technical University Berlin.
- On February 18, 2019 Larisa Adamyan successfully defended her dissertation "Adaptive weights community detection" at Humboldt-Universität zu Berlin (supervisor Vladimir Spokoiny).
- On March 14, 2019 Benjamin Stemper successfully defended her dissertation "Rough volatility models: Monte Carlo, asymptotics and deep calibration" at Technical University Berlin (supervisor Peter Friz and Christian Bayer).
- The Research Unit 2402 Rough paths, stochastic partial differential equeations and related topics was approved to be funded for another period. The research group contributes with the project "Numerical analysis of rough PDEs" (PIs: Christian Bayer, John Schoenmakers)
- Martin Redmann and Paolo Pigato received junior professorships in Halle and Rome, respectively.
- 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 presented at the 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 presented at the International Conference on Machine Learning 2019.
- The paper "A regularity structure for rough volatility" appeared in Mathematical Finance.
- 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