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Alexey Zaytsev

I have worked in Skoltech since 2017, starting as a junior research scientist and holding an assistant professor position now. My lab focuses on deep learning models for sequential data and robust machine learning. I am curious about engineering applications and fundamental questions in deep learning, looking for interesting problems that can lay a new foundation in deep learning and other sciences that benefit from it. These developments result in top-level publications, impactful industrial projects, empowering courses, and strong student theses. While the text below provides a nice overview of my achievements and research interests, you may also visit my lab’s website.

Research interests

My lab focuses on developing new models for sequential data and the robustness of deep learning models. This research is tightly connected to probabilistic machine learning and representation learning for various data modalities. Our joint publications with leading companies like Sberbank and Saudi Aramco pursue the desire to develop solutions that revolutionize the industry. On the other hand, we continue to publish at top deep learning venues, including KDD, AISTATS, and ACMM, and in top journals like IEEE Geoscience and Remote Sensing Letters and Journal of Petroleum Science and Engineering.

Industrial projects and further education

During the last few years, I have completed numerous industrial projects that earned tens of millions of dollars for significant banks, oil and gas companies, and telecom. As a lead in these projects, I collaborated with the industry on developing specifically tailored technologies based on machine learning and deep learning: production models are accurate and efficient. Their performance lasts long and delivers benefits to companies all this time. Another focus of my work is teaching others to work in this way. I developed modern courses for students and industrial partners at all levels, from engineers and experienced data scientists to top managers.


My alma mater was the Department of Control and Applied Mathematics of MIPT, which I completed in 2012. My master’s thesis proposed the Bayesian approach for linear regression that allows an automated feature selection. In my Ph.D. thesis, I obtained a new result on the effectiveness of Bayesian procedures for Gaussian process regression. These developments laid a theoretical justification for the selection of the design of experiments for variable fidelity models and proved minimax errors for Gaussian process regression. The publication venues for these results were several top journals and conferences, including the AISTATS conference. Working in a start-up, DATADVANCE, during my PhD, I took part in developing the pSeven library dedicated to data analysis for engineers. The pioneering tool for data fusion solved a regression problem for the case of data with more than one fidelity. Other applications of my work are now works in companies such as AREVA, TOTAL, and Airbus. In 2018, these results were awarded the Moscow government award for young scientists, “Development of predictive analytics methods for processing industrial, biomedical and economic data,” in joint with E. Burnaev and M. Panov.

Development of

  • reprepsentations for weakly structured data
  • anomaly detection approaches
  • processing of event sequences
  • change-point detection
  • adversarial attacks and adversarial training

Alexey also

  • leads Industrial projects for major industrial companies,
  • develops and provides further education courses for industrial partners of Skoltech
  1. Romanenkova, Evgenia, et al. InDiD: Instant Disorder Detection via Representation Learning. ACM Multimedia. 2022.
  2. Romanenkova, Evgenia, et al. Similarity learning for wells based on logging data. Journal of Petroleum Science and Engineering. 2022.
  3. Velikanov, Maksim, et al. Embedded Ensembles: infinite width limit and operating regimes. AISTATS. 022.
  4. I. Fursov, A. Zaytsev, R. Khasyanov, M. Spindler, E. Burnaev. Sequence Embeddings Help Detect Insurance Fraud. IEEE Access. 2022.
  5. I. Fursov, A. Zaytsev, N. Kluchnikov, A. Kravchenko, E. Burnaev. Differentiable Language Model Adversarial Attacks on Categorical Sequence Classifiers. IEEE Access. 2022
  6. S.C. Kumbhakar, A. Peresetsky, Y. Shchetynin, A. Zaytsev. Technical efficiency and inefficiency: Reassurance of standard SFA models and a misspecification problem. Econometrics and Statistics. 2021.
  7. I. Fursov, et al. “Adversarial attacks on deep models for financial transaction records.” Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2021.
  8. Kail, Roman, Evgeny Burnaev, and Alexey Zaytsev. Recurrent convolutional neural networks help to predict location of earthquakes. IEEE Geoscience and Remote Sensing Letters. 2021.
  9. V. Snorovikhina, A. Zaytsev. Unsupervised anomaly detection for discrete sequence healthcare data. AIST. 2020. 
  10. I. Fursov, A. Zaytsev, N. Kluchnikov, A. Kravchenko, E. Burnaev. Gradient-based adversarial attacks on categorical sequence models via traversing an embedded world. AIST. 2020
  11. E. Gurina, N. Klyuchnikov, A. Zaytsev et al. Application of machine learning to accidents detection at directional drilling. Journal of Petroleum Science and Engineering. 2020.
  12. E. Romanenkova, A. Zaytsev et al. Real-Time Data-Driven Detection of the Rock-Type Alteration During a Directional Drilling. IEEE Geoscience and Remote Sensing Letters. 2019.
  13. N. Klyuchnikov, A. Zaytsev et al. Data-driven model for the identification of the rock type at a drilling bit. Journal of Petroleum science and Engineering. 2019
  14. P. Proskura, A. Zaytsev, I. Braslavsky, E. Egorov, E. Burnaev. Usage of Multiple RTL Features for Earthquakes Prediction. ICMLA. 2019
  15. N. Kozlovskaya, A. Zaytsev. Deep Ensembles for Imbalanced Classification. ICMLA. 2017
  16. A. Zaytsev, E. Burnaev. Large scale variable fidelity surrogate modeling. Annals of Mathematics and Artificial Intelligence, 81: 167-186. 2017.
  17. A. Zaytsev, E. Burnaev. Minimax Approach to Variable Fidelity Data Interpolation. Artificial Intelligence and Statistics, 652-661. 2017.
  18. A. Zaytsev. Variable Fidelity Regression Using Low Fidelity Function Blackbox and Sparsification. Symposium on Conformal and Probabilistic Prediction with Applications, 147-164. 2016.
  19. A. Zaytsev. Reliable surrogate modeling of engineering data with more than two levels of fidelity. Mechanical and Aerospace Engineering (ICMAE), 2016 7th International Conference. 2016.
  20. A.A Zaitsev, E.V. Burnaev, V.G. Spokoiny. Properties of the posterior distribution of a regression model based on Gaussian random fields. Automation and Remote Control 74 (10), 1645-1655, 2013.
  21. E.V. Burnaev, A.A. Zaytsev, V.G. Spokoiny (alphabetic order of authors). The Bernstein-von Mises theorem for regression based on Gaussian Processes. Russ. Math. Surv 68 (5), 954-956, 2013.
  22. E.V. Burnaev, M.E. Panov, A.A. Zaytsev. Regression on the basis of nonstationary Gaussian processes with Bayesian regularization. Journal of Communications Technology & Electronics 61 (6), 661. 2016.
  23. E.V. Burnaev, A.A. Zaytsev. Surrogate modeling of multifidelity data for large samples. Journal of Communications Technology & Electronics 60 (12), 1348. 2015.
  24. A.A. Zaytsev, E.V. Burnaev, V.G. Spokoiny. Properties of the Bayesian parameter estimation of a regression based on Gaussian processes. J. Math. Sci 203 (6), 789-798. 2014.


  • Masters in Math at MIPT
  • PhD in Statistics and Machine Learning at IITP RAS

Industrial companies:

  • Airbus

Banks and related companies:

  • BPCE (top 3 bank in France)
  • Lending club
  • Sberbank

In 2018 Alexey was awarded the Moscow government award for young scientists “Development of predictive analytics methods for processing industrial, biomedical and economic data” joint with E. Burnaev and M. Panov.

Present students (with the year of the planned defense)

  • 2026, Vladislav Zhuzel, Skoltech, PhD
  • 2025, Evgneiya Romanenkova, Skoltech, PhD
  • 2025, Nikita Balabin, Skoltech, PhD
  • 2022, Matvey Morozov, Skoltech, Master
  • 2021, Roman Kail, MIPT, Bachelor
  • 2021, Nina Kaploukhaya, MIPT, Bachelor
  • 2022, Elizaveta Kovtun, Skoltech, Master
  • 2022, Pavel Burnyshev, Skoltech, Master
  • 2022, Valerii Baianov, Skoltech, Master (joint with Sber)
  • 2022, Pavel Shatalov, Skoltech, Master (joint with Sber)
  • 2022, Ekaterina Orlova, Skoltech, Master (joint with Sber)
  • 2022, Mark Zakharov, Skoltech, Master (joint with Sber)
  • 2022, Artur Bulanbaev, Skoltech, Master (joint with Sber)

Past students

  • 2021 Polina Proscura, MIPT, Master, 8 (out of 10)
  • 2021, Nikita Bekezin, Skoltech, Master, A (joint with Sber)
  • 2021, Maria Begicheva, Skoltech, Master, B (joint with Sber)
  • 2021, Anna Nikolaeva, Skoltech, Master, A (joint with Sber)
  • 2021, Artem Zabolotny, Skoltech, Master, A (joint with Sber)
  • 2021, Mike Vasilkovsy, Skoltech, Master, A
  • 2021, Maria Kuzmina, Skoltech, Master, B
  • 2020 Ivan Fursov, Skoltech, Master, A (joint with E.Burnaev)
  • 2020 Viktoria Snorovikhina, Skoltech, Master, B
  • 2019 Evgneiya Romanenkova, IITP, Master, A
  • 2019 Vladimir Shenderov, Skoltech, Master, B (joint with L.Ismailova)
  • 2019 Rasul Khasianov, Skoltech, IITP, Master, B
  • 2018 Oleg Alenkin, Skoltech, Master, B. Bayesian Optimization of SHiP Elements (joint with A.
  • 2017 Natalya Kozlovskaya, HSE, Master, A. Deep Boosting for Imbalanced Classification (joint
    with E.Burnaev)
  • 2017 Lee Yunjeong, Skoltech, Master, B. Gas Consumption Forecast in China (joint with K.Letova)
  • 2020 Matvey Morozov, IITP, Bachelor, A
  • 2020 Timur Aminov, MIPT, Bachelor, A
  • 2019 Polina Proscura, IITP, Bachelor, A
  • 2014 Yermek Kapushev, MIPT, Bachelor, A. Multifidelity Gaussian Processes (joint with E.Burnaev)
  • Courses on sequential data processing:
  • The craft of data visualization: