Stream Data Analytics
Machine Learning

High-level international research and development in Deep & Quantum Machine Learning
led by young researchers hosted at the world-wide recognized Center of Science - Akademgorodok
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Stream Data Analytics

We analyze Video-, Audio- and Textual data
To discover information in it related to domain problems, like:
* Brain Tumor Segmentation (CV),
* Human Speech Recognition (STT),
* Named Entity Recognition (NLP),
* Automatic Speech recognition (ASR)

Machine Learning

We employ Bayesian and Quantum approach along with the following methods:
* Interpretability
* Multi-task Learning
* Zero-Shot Learning
* Quantum Machine Leaning
* Multimodality


True Data Scientist solves the problem by combining the hardcore science and breakthrough data mining technologies with inexplicable art of human understanding. We propse an unique combination of Deep learning technique with a strict explainability of original FRiS method. Function of Rival Similarity allows us to make discovered patterns undestandable, which makes it a competetive advantage in case of reasonable decision making.

Software Engineering

Storage Technologies, Project management, Python.

Business Analytics

Business Analysis, Business Scaling, Knowledge Management, Data Warehousing.

Deep Learning

Statistics & Algorithms, Machine Learning, Natural Language Processing, Artificial Intelligence, Decision-Making Theory, Signal Processing, and Deep Learning.


We provide a high level of international research based on best practices from Industry and Siberian Branch of Russian Academy of Sciences.

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Evgeniy Pavlovskiy

PhD in Math, Leading researcher

Bair Tuchinov

Head of the laboratory

Vladimir Groza

Ph.D., Leading Researcher, CV and Health Domain

Evgeniya Amelina

Ph.D., Scientific Researcher in Healthcare domain

Yuliya Rubtsova

Ph.D., Scientific Researcher,NVidia Deep Learning Institute certified NVidia DLI specialist

Sergey Pnev

B.Sc., Research Engineer

Daniil Grebenkin

B.Sc., Research Engineer

Daria Nosenko

B.Sc., Research Engineer

Nikolay Tolstokulakov

NVidia Deep Learning InstituteCertified DLI NVidia teacher, CV Researcher.

Akilesh Sivaswamy

MSc, Research engineer, superpostition expert

Welcome to our team!

Selected publications

1) Tuchinov, B. N., Pavlovskiy, E., Amelin, M. E., Tolstokulakov, N. Y., Golushko, S. K., Amelina, E. V., & Groza, V. (2021). Brain Tumor Segmentation and Associated Uncertainty Evaluation Using Multi-sequences MRI Mixture Data Preprocessing. [WebOfScience] SJR2020=0.25

2) Tsvaki, J. J., Tailakov, D. O., & Pavlovskiy, E. N. (2020). Development of water flood model for oil production enhancement. In Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020 (pp. 46-49). [9303200] (Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020). Institute of Electrical and Electronics Engineers Inc.. [Scopus]

3) Letyagin, A., Golushko, S., Amelin, M., Tuchinov, B., Amelina, E., Tolstokulakov, N., ... & Groza, V. (2020, July). Multi-class Brain Tumor Segmentation via Multi-sequences MRI Mixture Data Preprocessing. In 2020 Cognitive Sciences, Genomics and Bioinformatics (CSGB) (pp. 185-189). IEEE. DOI: 10.1109/CSGB51356.2020.9214645, [Scopus]

4) Tolstokulakov, N., Pavlovskiy, E., Tuchinov, B., Amelina, E., Amelin, M., Letyagin, A., Golushko, S., Groza, V. Data Preprocessing Via Compositions Multi-Channel MRI Images to Improve Brain Tumor Segmentation // (2020) ISBI Workshops 2020 - International Symposium on Biomedical Imaging Workshops, Proceedings, DOI: 10.1109/ISBIWorkshops50223.2020.9153416. [Scopus]

5) Groza, V., Tuchinov, B., Pavlovskiy, E., Amelina, E., Amelin, M., Golushko, S., Letyagin, A. Data Preprocessing via Multi- sequences MRI Mixture to Improve Brain Tumor Segmentation // (2020) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12108 LNBI, pp. 695-704. DOI: 10.1007/978-3-030-45385-5_62. SJR2020=0.15 [Scopus]

6) Letyagin, A.Y., Degtyareva, L.O., Golushko, S.K., Rzaev, J.A., Amelin, M.E., Pavlovsky, E.N., Tuchinov, B.N., Amelina, E.V., Moisak, G.I., Bulgakova, E.G. Artificial Intelligence for Imaging Diagnostics in Neurosurgery // (2019) SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings, pp. 336-337. Cited 1 time. , DOI: 10.1109/SIBIRCON48586.2019.8958201 SJR2020=0.15 [Scopus]

7) Sheng, L. & Pavlovskiy, E. N., Reducing over-smoothness in speech synthesis using Generative Adversarial Networks. Oct. 2019, 2019 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON). IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, pp. 972-974, 3 p. DOI: 10.1109/SIBIRCON48586.2019.8957862, URL: SJR2020=0.15 [Scopus]

8) E. N. Pavlovskiy, "Problems and Prospectives of Big Data Storage and Processing Standartization," 2019 International Multi- Conference on Engineering, Computer and Information Sciences (SIBIRCON), Novosibirsk, Russia, 2019, pp. 0995-0998, doi: 10.1109/SIBIRCON48586.2019.8958046. URL: . SJR2020=0.15 [Scopus]

9) S.A. Alyamkin, N.A. Nikolenko, E.N. Pavlovskiy, V.V. Dyubanov. FRiS-censoring of reference sample in face recognition task by deep neural networks // Data Science and Engineering (SSDSE), 2017 Siberian Symposium on. IEEE. — 2017. URL: . SJR2020=0.16 [WebOfScience] [Scopus]

10) Pavlovsky E. N., Pakulich D. V., Pospelov S. O. Restoration of the 3D Skull Defect Model Based on Deep Neural Networks. Vestnik NSU. Series: Information Technologies, 2017, vol. 15, no. 3, p. 74–78. (In Russ.) URL:


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