Areas of Research
- Quantification of uncertainty in estimation of covariance matrices and their spectral projectors; applications of efficient dimension reduction
- Community detection and clustering
- Topological data analysis
- Data analysis based on Monge-Kantorovich spaces geometry
- Inference for discretely observed stochastic processes
- Uncertainty quantification and variance reduction for MCMC algorithms
- Statistical inference for McKean-Vlasov-SDEs
Scientific reports of the laboratory
- Uncertainty quantification in machine learning algorithms, 2021
- Uncertainty quantification in high-dimensional models, 2020
- Uncertainty quantification in high-dimensional statistics, 2019
RSF grant № 19-71-30020, "Applications of probabilistic artificial neural generative models to development of digital twin technology for non-linear stochastic systems", 2019 - 2022 (HSE University)
RSF grant № 18-11-00132, "Analysis of high dimensional random objects with applications to large-scale data processing", 2018-2020 (HSE University) grant leader: A.Naumov, PI's: V. Spokoiny, D. Belomestny
Presidents of Russian Federation Grant for young scientists № 4596.2016.1, "Local laws for random matrices and universality of local spectral statistics", 2016-2017 (Lomonosov MSU, Skoltech)
RFBR grant 16-31-00005 ”Spectral analysis of large dimensional random matrices”, 2016–2017 (Lomonosov MSU, Skoltech)
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