Personal Websites

Maxim Panov

Assistant Professor, Head of the Statistical Machine Learning Laboratory
Statistical Machine Learning Laboratory
Center for Artificial Intelligence Technology

Maxim received his Bachelor’s and Master’s degrees from the Moscow Institute of Physics and Technology in 2010 and 2012. His Bachelor thesis addressed the problem of filling missing data values in the support vector machine classification framework. The Master thesis focused on the Gaussian processes regression and adaptive design of experiments. In 2012, Maxim started his postgraduate studies and switched the direction to the field of Mathematical Statistics. He concentrated on obtaining tight bounds for the Gaussian approximation of posterior distribution (Bernstein-von Mises phenomenon). The research resulted in a series of publications in peer-reviewed journals. It formed the core of Maxim’s Candidate of Sciences thesis defended at the Institute for Information Transmission Problems in January 2016.

Starting in 2010, Maxim also worked part-time as a research scientist in DATADVANCE Company, a Skolkovo Innovation Center resident. There he participated in developing the library of data analysis methods for engineering applications. This library, pSeven, is now used by many companies worldwide, including Airbus, Porsche, Mitsubishi, Toyota, Limagrain, and many others.

Currently, Maxim works at Skoltech in Assistant Professor’s position and leads the Statistical Machine Learning group. His research is focused on Bayesian approaches in machine learning, uncertainty quantification, and graph analytics.


  • Thin, A., Kotelevskii, N., Durmus, A., Panov, M., Moulines, E. (2020). MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference. arXiv preprint arXiv:2002.1225.
  • Tsymbalov, E. Fedyanin, K., Panov, M. (2020). Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampled Implicit Ensembles. arXiv preprint arXiv:2003.03274.
  • Nuzhin, E., Panov, M., Brilliantov N. Why do animals swirl and how do they group? Submitted to Scientific Reports.
  • Thin, A., Kotelevskii, N., Panov, M., Andrieu, C., Durmus, A., Moulines, E. (2020) Nonreversible MCMC from conditional invertible transforms: a complete recipe with convergence guarantees. arXiv preprint arXiv:2012.15550.
  • G. Novikov, M. Panov and I. Oseledets “Tensor-Train Density Estimation”, submitted to UAI 2021.
  • A. Thin, N. Kotelevskii, A. Durmus, M. Panov, E. Moulines, A. Doucet “Monte Carlo Variational Auto-Encoders”, submitted to ICML, 2021.
  • I. Anokhin, R. Kail, M. Velikanov, M. Panov, A. Zaytsev and D. Yarotsky “M-Ensembles: Accuracy of Standard Ensembles at the Cost of (Almost) a Single Model”, submitted to ICML, 2021.

To appear

  • Ivan Nazarov, Maria Burkina, Boris Shirokikh, Gennady Fedonin and Maxim Panov “Sparse-group inductive matrix completion”, arXiv preprint arXiv:1804.10653, accepted to Journal of Computational Mathematics and Mathematical Physics, 2021.
  • Spokoiny, V., Panov, M. “Accuracy of Gaussian approximation in nonparametric Bernstein–von Mises Theorem”. arXiv preprint arXiv:1910.06028, accepted to Bernoulli, 2021.
  • Artem Shelmanov, Evgenii Tsymbalov, Dmitri Puzyrev, Kirill Fedyanin, Alexander Panchenko, and Maxim Panov “How Certain is Your Transformer?”, accepted to EACL, 2021.

Machine learning

  • Ivan Sukharev, Valentina Shumovskaia, Kirill Fedyanin, Maxim Panov, and Dmitry Berestnev “EWS-GCN: Edge Weight-Shared Graph Convolutional Network for Transactional Banking Data”, 2020 IEEE International Conference on Data Mining (ICDM), pp. 1268-1273.
  • Shumovskaia, V., Fedyanin, K., Sukharev, I., Berestnev, D., Panov, M. (2020). Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data. 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp. 787-788.
  • Artemenkov, A., Panov, M. NCVis: Noise Contrastive Approach for Scalable Visualization. In Proceedings of the Web Conference 2020, pp. 2941-2947.
  • Evgeny Tsymbalov, Sergey Makarychev, Alexander Shapeev and Maxim Panov “Deeper Connections between Neural Networks and Gaussian Processes Regression Speed-up Active Learning”, International Conference Joint on Artificial Intelligence, 2019.
  • Gomtsyan, M., Mokrov, N., Panov, M., & Yanovich, Y. (2019). Geometry-Aware Maximum Likelihood Estimation of Intrinsic Dimension. Proceedings of The Eleventh Asian Conference on Machine Learning, PMLR, 101, 1126-1141.
  • Konstantin Slavnov and Maxim Panov “Overlapping Community Detection in Weighted Graphs: Matrix Factorization Approach”, Proceedings of the 11th International Conference on Intelligent Data Processing: Theory and Applications, Springer, 2019.
  • Stanislav Tsepa and Maxim Panov “Constructing Graph Node Embeddings via Discrimination of Similarity Distributions”, IEEE International Conference on Data Mining WorkshopsICDMW, 1050-1053, 2018.
  • Evgenii Tsymbalov, Maxim Panov and Alexander Shapeev “Dropout-based Active Learning for Regression”, Proceedings of 7th International Conference on Analysis of Images, Social Networks and Texts, 247–258, Lecture Notes in Computer Science, Springer, 2018
  • Panov M., Burnaev E., Yarotsky D., Belyaev M., Kapushev E., Vetrov D., Prikhodko P. GTApprox: Surrogate modeling for industrial design // Advances in Engineering Software. 2016. Vol. 102. P. 29-39.
  • Panov M., Burnaev E., Zaytsev A. Regression on the basis of nonstationary Gaussian processes with Bayesian regularization // Journal of Communications Technology and Electronics. 2016. Vol. 61. No. 6. P. 661-671.
  • Burnaev E., Panov M. Adaptive Design of Experiments Based on Gaussian Processes, in: Lecture Notes in Computer Science Vol. 9047: Statistical Learning and Data Sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings. Switzerland : Springer International Publishing, 2015. P. 116-125.
  • Panov M., Tatarchuk A., Mottl V., Windridge D. A Modified Neutral Point Method for Kernel-Based Fusion of Pattern-Recognition Modalities with Incomplete Data Sets, in: Lecture Notes in Computer Science Vol. 6713: Multiple Classifier Systems: 10th International Workshop, MCS 2011, Naples, Italy, June 15-17, 2011. Proceedings. Springer Berlin Heidelberg, 2011. P. 126-136.



  • S. Fedorov, M.Y. Vasilkov, M. Panov, D. Rupasov, A. Rashkovskiy, N.M. Ushakov, T. Kallio, J. Lee, R. Hempelmann, A.G. Nasibulin “Tailoring electrochemical efficiency of hydrogen evolution by fine design of TiOx/RuOx composite cathode architecture”, International Journal of Hydrogen Energy44(21), 10593–10603, 2019.
  • Menshchikov, A., Ermilov, D., Dranitsky, I., Kupchenko, L., Panov, M., Fedorov, M., Somov A. (2019). Data-Driven Body-Machine Interface for Drone Intuitive Control through Voice and Gestures. IECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society, 5602-5609.
  • Nikita Mokrov, Maxim Panov, Boris A. Gutman, Joshua I. Faskowitz, Neda Jahanshad and Paul M. Thompson “Simultaneous Matrix Diagonalization for Structural Brain Networks Classification”, Proceedings of the 6th International Conference on Complex Networks and Their Applications, 2017. Studies in Computational Intelligence, volume 689, pp. 1261 – 1270. Springer.
  • Dmitry Ermilov, Maxim Panov and Yury Yanovich “Automatic Bitcoin Address Clustering”, Proceedings of the 16th IEEE International Conference on Machine Learning and Applications (ICMLA), Cancun, 2017, pp. 461-466.
  • Bayesian methods in machine learning and statistics
  • Uncertainty estimation for machine learning models
  • Algorithms and statistical analysis of complex networks
  • Gaussian processes regression
  • Statistical inference, semiparametric inference
  • Moscow Government Award for Young Scientists 2018 (together with Evgeny Burnaev and Alexey Zaytsev)
  • RFBR grant for young scientists 2018-2019
  • RSF grant for scientific groups led by young scientists 2020 – 2023

Courses I taught:

  • Introduction to Data Science, Skoltech (Fall 2016-2020)
  • Principles of Applied Statistics, Skoltech (Fall 2020)
  • Uncertainty Quantification, Skoltech (Fall 2019)
  • Introduction to Data Science, Moscow Institute of Physics and Technology (Spring 2015-2020)
  • Applied Statistics, Moscow Institute of Physics and Technology (Fall 2011-2018, Spring 2020-2021) and Higher School of Economics (Fall 2016)
  • Practical work classes in statistics, Moscow Institute of Physics and Technology (Spring 2012-2013)

Also, I am actively involved in coordination and participation in program creation for Data Science and Information Science and Technology MSc programs and Computational and Data Science and Engineering PhD program at Skoltech (2016 – present)

Service for community

Associate editor at Journal of Statistical Planning and Inference.

Reviewer for

  • Automation and Remote Control
  • Bernoulli
  • International Journal of Machine Learning and Cybernetics
  • Journal of Nonparametric Statistics
  • Neural Information Processing Systems
  • International Conference on Machine Learning
  • International Conference on Learning Representations