Vladimir Spokoiny

Prof. Vladimir Spokoiny is a leading figure in stochastic algorithms and nonparametric statistics at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) and Humboldt University of Berlin. His work bridges mathematical statistics with practical applications in finance, medicine, and machine learning.

• Born in 1959 in Moscow, USSR
• PhD from Lomonosov Moscow State University (1988)
• Habilitation from Humboldt University (1996)
• Head of WIAS research group since 2000
• Professor at Humboldt University since 2002

Spokoiny's research focuses on adaptive nonparametric methods, high-dimensional data analysis, and statistical finance. His innovations in local homogeneity testing and propagation-separation methods have advanced volatility modeling, image analysis, and manifold learning. He employs Bayesian optimization frameworks and stochastic control techniques for financial instrument pricing.

Recent scientific contributions include generalized bootstrap procedures for Bures-Wasserstein barycenters (2024), dimension-free Laplace approximation bounds (2023), and structure-adaptive manifold estimation (2022). His 19+ PhD students and editorial roles in top journals like The Annals of Statistics demonstrate sustained academic impact.

• International Statistical Institute member
• American Statistical Association fellow
• Institute of Mathematical Statistics member
• Bernoulli Society member