Alexey Naumov
- Laboratory Head:Faculty of Computer Science / International Laboratory of Stochastic Algorithms and High-Dimensional Inference
- Assistant Professor:Faculty of Computer Science / Department of Technologies for Complex System Modelling
- Programme Academic Supervisor:Statistical Learning Theory
- Alexey Naumov has been at HSE University since 2016.
Education and Degrees
- 2013
PhD
Bielefeld University - 2013
Candidate of Sciences* (PhD)
Lomonosov Moscow State University - 2010
Degree
Lomonosov Moscow State University
According to the International Standard Classification of Education (ISCED) 2011, Candidate of Sciences belongs to ISCED level 8 - "doctoral or equivalent", together with PhD, DPhil, D.Lit, D.Sc, LL.D, Doctorate or similar. Candidate of Sciences allows its holders to reach the level of the Associate Professor.
Continuing education / Professional retraining / Internships / Study abroad experience
Chinese University of Hong Kong, Department of Statistics, Hong Kong, Sep. 2015 -- Dec. 2015.
Bielefeld University, Faculty of Mathematics, Jan. 2013 -- Nov. 2013.
Student Term / Thesis Papers
- Master
D. Demidova «Statistical Analysis of Topological Descriptors». Faculty of Computer Science, 2019
M. Yakhlakov «Gaussian Approximation for a Large Number of Linear or Non-linear Forms with Applications to Statistical Inference». Faculty of Computer Science, 2019
S. Samsonov «Concentration Inequalities for Functionals of Markov Chains With Applications to Variance Reduction». Faculty of Computer Science, 2019
V. Shumovskaia «Hypothesis Testing for Random Graphs with Community Structure». Faculty of Computer Science, 2019
Y. Tavyrikov «Approximation of Dynamic Programming Problems for Estimating the Value of an Option». Faculty of Computer Science, 2018
Courses (2019/2020)
- Modern Methods of Data Analysis: Stochastic Calculus (Master’s programme; Faculty of Computer Science; 1 year, 1, 2 module)Eng
- Random Matrix Theory (Master’s programme; Faculty of Computer Science; 2 year, 1 module)Eng
- Research Seminar (Master’s programme; Faculty of Computer Science; 2 year, 1-3 module)Eng
- Past Courses
Courses (2018/2019)
- Calculus 2 (Bachelor’s programme; Faculty of Computer Science; 2 year, 1-4 module)Rus
- Modern Methods of Data Analysis: Stochastic Calculus (Master’s programme; Faculty of Computer Science; 1 year, 1, 2 module)Eng
- Random Matrix Theory (Master’s programme; Faculty of Computer Science; 2 year, 1, 2 module)Eng
Courses (2017/2018)
- Modern Methods of Data Analysis: Stochastic Calculus (Master’s programme; Faculty of Computer Science; 1 year, 1, 2 module)Eng
- Random Matrix Theory (Master’s programme; Faculty of Computer Science; 2 year, 3 module)Rus
Courses (2016/2017)
- Probability Theory and Mathematical Statistics (Bachelor’s programme; Faculty of Computer Science; 2 year, 1-4 module)Rus
- Random Matrix Theory (Master’s programme; Faculty of Computer Science; 2 year, 1, 2 module)Rus
Publications17
- Article Naumov A., Spokoiny V., Ulyanov V. V. Bootstrap confidence sets for spectral projectors of sample covariance // Probability Theory and Related Fields. 2019. Vol. 174. No. 3-4. P. 1091-1132. doi
- Article Goetze F., Naumov A., Spokoiny V., Ulyanov V. V. Large ball probability, Gaussian comparison and anti-concentration // Bernoulli: a journal of mathematical statistics and probability. 2019. Vol. 25. No. 4(A). P. 2538-2563. doi
- Article Naumov A., Tikhomirov A., Гётце Ф. Local Semicircle Law Under Fourth Moment Condition // Journal of Theoretical Probability. 2019. P. 1-36. doi (in press)
- Article Naumov A., Tikhomirov A., Гётце Ф. On Local laws for non-Hermitian random matrices and their products // Random Matrices-Theory and Applications. 2019. P. 1-51. doi (in press)
- Article Naumov A., Tikhomirov A., Гётце Ф. On Optimal Bounds in the Local Semicircle Law under Four Moment Condition / Пер. с рус. // Doklady Mathematics. 2019. Vol. 99. No. 1. P. 40-43. doi
- Article Belomestny D., Moulines E., Iosipoi L., Naumov A., Samsonov S. Variance reduction for Markov chains with application to MCMC // Working papers by Cornell University. Series math "arxiv.org". 2019. P. 1-32.
- Article Naumov A., Spokoiny V., Ulyanov V. V. Confidence Sets for Spectral Projectors of Covariance Matrices / Пер. с рус. // Doklady Mathematics. 2018. Vol. 98. No. 2. P. 511-514. doi
- Article Naumov A., Tikhomirov A., Goetze F. Local Semicircle Law under Moment Conditions: The Stieltjes Transform, Rigidity, and Delocalization // Theory of Probability and Its Applications. 2018. Vol. 62. No. 1. P. 58-83. doi
- Article Naumov A., Spokoiny V., Ulyanov V. V., Tavyrikov Y. Nonasymptotic Estimates for the Closeness of Gaussian Measures on Balls, / Пер. с рус. // Doklady Mathematics. 2018. Vol. 98. No. 2. P. 490-493. doi
- Article Goetze F., Naumov A.A., Tikhomirov A. On the local semicircular law for Wigner ensembles // Bernoulli: a journal of mathematical statistics and probability. 2018. Vol. 24. No. 3. P. 2358-2400. doi
- Article Ulyanov V. V., Goetze F., A. Naumov. Asymptotic analysis of symmetric functions // Journal of Theoretical Probability. 2017. Vol. 30. No. 3. P. 876-897. doi
- Article Goetze F., Naumov A.A., Tikhomirov A. Distribution of linear statistics of singular values of the product of random matrices // Bernoulli: a journal of mathematical statistics and probability. 2017. Vol. 23. No. 4B. P. 3067-3113. doi
- Article Goetze F., Naumov A., Tikhomirov A. Local Laws for Non-Hermitian Random Matrices // Doklady Mathematics. 2017. Vol. 96. No. 3. P. 558-560. doi
- Article Naumov A., Гётце Ф., Tikhomirov A. Local Semicircle Law under Weak Moment Conditions / Пер. с рус. // Doklady Mathematics. 2016. Vol. 93. No. 3. P. 248-250. doi
- Article Naumov A., Tikhomirov A., Гётце Ф. LIMIT THEOREMS FOR TWO CLASSES OF RANDOM MATRICES WITH DEPENDENT ENTRIES / Пер. с рус. // Theory Probability and its Applications. 2015. Vol. 59. No. 1. P. 23-39. doi
- Article Naumov A., Tikhomirov A., Гётце Ф. ON A GENERALIZATION OF THE ELLIPTIC LAW FOR RANDOM MATRICES // Acta Physica Polonica, Series B.. 2015. Vol. 46. No. 9. P. 1737-1745. doi
- Article Goetze F., Naumov A.A., Tikhomirov A. On minimal singular values of random matrices with correlated entries // Random Matrices-Theory and Applications. 2015. Vol. 4. No. 2 doi
Grants
RSF grant № 18-11-00132, "Analysis of high dimensional random objects with applications to large-scale data processing", 2018-2020 (HSE)
Presidents of Russian Federation Grant for young scientists № 4596.2016.1, "Local laws for random matrices and universality of local spectral statistics", 2016-2017 (MSU, Skoltech)
RFBR grant 16-31-00005 ”Spectral analysis of large dimensional random matrices”, 2016–2017 (MSU, Skoltech)
Employment history
Institute for information transmission problems (IITP RAS), senior research scientist, 2015 - current
Skolkovo Institute of Science and Technology (Skoltech), senior research scientist, 2016-2018
Moscow State University, Faculty of computational mathematics and cybernetics, assistant professor, 2013 - 2016
First Cohort Graduates from Master’s Programme in Statistical Learning Theory
The Master's Programme in Statistical Learning Theory was launched in 2017. It is run jointly with the Skolkovo Institute of Science and Technology (Skoltech). The programme trains future scientists to effectively carry out fundamental research and work on new challenging problems in statistical learning theory, one of the most promising fields of science. Yury Kemaev and Maxim Kaledin, from the first cohort of programme graduates, sat down with HSE News Service to talk about their studies and plans for the future.
First Cohort Graduates from Master’s Programme in Statistical Learning Theory
The Master's Programme ‘Statistical Learning Theory’ was launched in 2017, and is run jointly with the Skolkovo Institute of Science and Technology(Skoltech).
HDI Lab staff attend International Vilnius Conference on Probability Theory and Mathematical Statistics 2018
12th International Vilnius Conference on Probability Theory and Mathematical Statistics and 2018 IMS Annual Meeting on Probability and Statistics took place in Vilnius (Lithuania) on July 2-6. This is one of the world's leading conferences in the field of modern probability theory and mathematical statistics, which is held every four years since 1973. This year over 200 works were presented at the event and 500 participants from all over the globe attended it.
Structural Learning Seminar Summer Meeting
On 19 July at 11 am an extraordinary meeting of Structural Learning Seminar was held on the Faculty of Computer Science.
Laboratory Researchers Received Russian Science Foundation Grant
Team of researchers of the HSE International Laboratory of Stochastic Algorithms and High-Dimensional Inference was announced as a winner of the Russian Science Foundation Grant Competition to support fundamental and exploratory scientific research conducted by individual scientific groups and was awarded three-year grant for implementation of the project "Analysis of high dimensional random objects with applications to large scale data processing" (RSF №18-11-00132).
HSE Lends Its Support to the Very First Conference in ‘New Frontiers in High-Dimensional Probability and Statistics’
On February 23 and 24, the Institute for Information Transmission Problems of the Russian Academy of Sciences hosted the first international mini-conference entitled ‘New frontiers in high-dimensional probability and statistics’. The event was attended by Russian and international researchers in the field of statistical methods of analysis of multidimensional data and modern stochastic algorithms. The conference was hosted by HSE, the Institute for Information Transmission Problems of the RAS and Skoltech. Organisers included HSE Faculty of Computer Science staff, Vladimir Spokoiny, Alexey Naumov, Denis Belomestny and Quentin Paris.
‘Our Programme Aims to Make a Research Breakthrough at the Intersection of Mathematics and Computer Science’
In 2017, the HSE Faculty of Computer Science and Skoltech are opening admissions to the Master’s programme inStatistical Learning Theory, which will become the successor to theMathematical Methods of Optimization and Stochastics programme.Vladimir Spokoiny, the programme’s academic supervisor and professor of mathematics at Humboldt University in Berlin, told us about the research part of the new programme and the opportunities it offers to both Master’s students and undergraduate students alike.
