## Research Interests

- Algorithms and statistical analysis for random graphs
- Bayesian methods in machine learning and statistics
- Nonparametric and semiparametric statistical inference

## Teaching

- Introduction to Data Science (Spring 2015-2017 at Phystech, Fall 2016-2017 at Skoltech)

- Applied Statistics (Fall 2011-2017 at Phystech, Fall 2016 at HSE)

## Students

Current:

- Maria Burkina
- Dmitry Ermilov
- Alfredo de la Fuente
- Marina Gomtsyan
- Anastasia Koloskova
- Evgeny Marshakov
- Nikita Mokrov
- Mikhail Pautov
- Stanislav Tsepa
- Roman Ushakov

Past:

- Kirill Kuznetsov (MSc, HSE 2017)
- Konstantin Slavnov (MSc, HSE 2017)
- Anton Votinov (MSc, HSE 2017)
- Igor Silin (BSc, MIPT 2016)

## Current projects

- Provable overlapping community detection
- Sparse inductive matrix completion
- Graph nodes embeddings

## For prospective students

Here I summarized ideas for several research projects which can be conducted under my supervision:

- Model selection in overlapping community detection
- Semi-supervised nodes classification
- Local message-passing algorithms for community detection and classification

## Publications

##### 2017:

- Maxim Panov, Konstantin Slavnov and Roman Ushakov “Consistent Estimation of Mixed Memberships with Successive Projections” Proceedings of Complex Networks 2017 (The 6th International Conference on Complex Networks and Their Applications) (arXiv:1707.01350).
- Nikita Mokrov and Maxim Panov “Simultaneous Matrix Diagonalization for Structural Brain Networks Classification” Proceedings of Complex Networks 2017 (The 6th International Conference on Complex Networks and Their Applications) (arXiv:1710.05213).
- Dmitry Ermilov, Maxim Panov and Yury Yanovich “Automatic Bitcoin Address Clustering”, International Conference of Machine Learning and Applications, 2017.
- Konstantin Slavnov and Maxim Panov “Overlapping Community Detection in Weighted Graphs: Matrix Factorization Approach”, Proceedings of IIP conference, Springer, 2017.

##### 2016:

- M. Belyaev, E. Burnaev, E. Kapushev, M. Panov, P. Prikhodko, D. Vetrov, D. Yarotsky. GTApprox: Surrogate Modeling for Industrial Design. Advances in Engineering Software. — 2016. — Vol. 102. — Pp. 29–39.

- M. Panov. Nonasymptotic approach to Bayesian semiparametric inference. Doklady Mathematics, 93 (2), pp. 155-158.

- E. Burnaev, A. Zaytsev, M. Panov. Regression on the Basis Nonstationary Gaussian Processes with Bayesian Regularization. Journal of Communications Technology and Electronics, 61(6), pp 661-671.

##### 2015:

- M. Panov, V. Spokoiny. Finite Sample Bernstein – von Mises Theorem for Semiparametric Problems. Bayesian analysis, 10(3), pp. 665–710.

- E. Burnaev, M. Panov. Adaptive Design of Experiments based on Gaussian Processes. Proceedings of Symposium on Statistical Learning and Data Sciences, Lecture notes in computer science, pp. 116-125.

##### 2014:

- M. Panov, V. Spokoiny. Critical Dimension in Semiparametric Bernstein – von Mises Theorem. Proceedings of Steklov mathematical Institute, 287, pp. 242–266.

##### 2011:

- M. Panov, A. Tatarchuk, V. Mottl, D. Windridge. A Modified Neutral Point Method for Kernel-based Fusion of Pattern-Recognition Modalities with Incomplete Data Sets. Proceedings of the 10th International Workshop MCS 2011, Naples, Italy, June 15-17, 2011. Proceedings. Springer Berlin Heidelberg, 2011. P. 126-136.

## Maxim Panov

## Research Scientist

Joint affiliation with a group of Prof. Maxim Fedorov

m.panov@skoltech.ru

Phone: +7 (495) 280 14 81 ext. 3504.

Office: 335.

Skolkovo Institute of Science and Technology, Skolkovo Innovation Center, Building 4, Moscow, 143026, Russian Federation.