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NSS Team

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Here are some of our projects:

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FEDOT is a framework for automated machine learning. It can build composite pipelines for the different real-world processes in an automated way using an evolutionary approach.

Nikitin N. O. et al. Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelines // Future Generation Computer Systems. – 2021.

Sarafanov M., Nikitin N. O., Kalyuzhnaya A. V. Automated data-driven approach for gap filling in the time series using evolutionary learning //arXiv preprint arXiv:2103.01124. – 2021.

Nikitin N. O. et al. Hybrid and Automated Machine Learning Approaches for Oil Fields Development: the Case Study of Volve Field, North Sea //arXiv preprint arXiv:2103.02598. – 2021.

Polonskaia I. S. et al. Multi-Objective Evolutionary Design of Composite Data-Driven Models //arXiv preprint arXiv:2103.01301. – 2021.

Kalyuzhnaya A. V. et al. Towards Generative Design of Computationally Efficient Mathematical Models with Evolutionary Learning //Entropy. – 2021. – Т. 23. – №. 1. – С. 28.

Nikitin N.O., Polonskaia I.S., Vychuzhanin P., Barabanova I.V., Kalyuzhnaya A.V. Structural Evolutionary Learning for Composite Classification Models //Procedia computer science. – 2020. – Т. 178. – C. 414-423.

Kalyuzhnaya A. V. et al. Automatic evolutionary learning of composite models with knowledge enrichment //Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. – 2020. – С. 43-44.

Nikitin N. O. et al. Evolutionary ensemble approach for behavioral credit scoring //International Conference on Computational Science. – Springer, Cham, 2018. – С. 825-831.

The repository is available in github. You can find documentation here.

Bayesian Analytical and Modelling Toolkit

It is a data modeling and analysis tool based on Bayesian networks. It can be divided into two main parts - algorithms for constructing and training Bayesian networks on data and algorithms for applying Bayesian networks for filling gaps, generating synthetic data, and searching for anomalous values.

Deeva I., Andriushchenko P.D., Kalyuzhnaya A.V., Boukhanovsky A.V. Bayesian Networks-based personal data synthesis // ACM International Conference Proceeding Series - 2020, pp. 6-11

Andriushchenko P.D.,Deeva I.Y., Kalyuzhnaya A.V., Bubnova А.V., Voskresensky А.G., Bukhanov N.V. Analysis of parameters of oil and gas fields using Bayesian networks // Data Science in Oil and Gas 2020 - 2020. - С. 1-10

Deeva I. et al. Oil and Gas Reservoirs Parameters Analysis Using Mixed Learning of Networks //arXiv preprint arXiv:2103.01804. – 2021.

The repository is available in github

EPDE framework

EPDE is a framework for differential equation discovery, that inspires researches to create new models in an understandable mathematical form.

Kalyuzhnaya A. V. et al. Automatic evolutionary learning of composite models with knowledge enrichment //Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. – 2020. – С. 43-44.

Maslyaev M., Hvatov A., Kalyuzhnaya A. Data-Driven Partial Differential Equations Discovery Approach for the Noised Multi-dimensional Data //International Conference on Computational Science. – Springer, Cham, 2020. – С. 86-100.

Merezhnikov M., Hvatov A. Closed-form algebraic expressions discovery using combined evolutionary optimization and sparse regression approach // Procedia Computer Science. – 2020. – Т. 178. – С. 424-433.

Maslyaev M., Hvatov A., Kalyuzhnaya A. Discovery of the data-driven models of continuous metocean process in form of nonlinear ordinary differential equations //Procedia Computer Science. – 2020. – Т. 178. – С. 18-26.

Hvatov A., Maslyaev M. The data-driven physical-based equations discovery using evolutionary approach // Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. - 2020. - C. 129–130.

Maslyaev M., Hvatov A., Kalyuzhnaya A. Data-Driven Partial Derivative Equations Discovery with Evolutionary Approach //International Conference on Computational Science. – Springer, Cham, 2019. – С. 635-641.

Maslyaev M., Hvatov A. Discovery of the data-driven differential equation-based models of continuous metocean process //Procedia Computer Science. – 2019. – Т. 156. – С. 367-376.

For more information, see FEDOT.Algs/

Metocean Simulation

Creation and calibration of metocean simulation models for data restoration, anomalies or patterns detection

Sarafanov M. et al. A Machine Learning Approach for Remote Sensing Data Gap-Filling with Open-Source Implementation: An Example Regarding Land Surface Temperature, Surface Albedo and NDVI //Remote Sensing. – 2020. – Т. 12. – №. 23. – С. 3865.

Araya-Lopez J. L., Nikitin N. O., Kaluzhnaya A. V. Case-adaptive ensemble technique for metocean data restoration //Procedia Computer Science. – 2018. – Т. 136. – С. 311-320.

Deeva I., Nikitin N. O., Kaluyzhnaya A. V. Pattern recognition in Non-Stationary Environmental Time Series Using Sparse Regression //Procedia Computer Science. – 2019. – Т. 156. – С. 357-366.

Hvatov A. et al. Adaptation of NEMO-LIM3 model for multi-grid high-resolutional Arctic simulation //Ocean Modelling. – 2019. – Т. 141. – С. 101427. View on GitHub

Nikitin N. O. et al. The multi-objective optimisation of breakwaters using evolutionary approach //arXiv preprint arXiv:2004.03010. – 2020.

Nikitin N. O. et al. Deadline-driven approach for multi-fidelity surrogate-assisted environmental model calibration: SWAN wind wave model case study //Proceedings of the Genetic and Evolutionary Computation Conference Companion. – 2019. – С. 1583-1591. View on GitHub

Vychuzhanin P., Hvatov A., Kalyuzhnaya A. V. Anomalies Detection in Metocean Simulation Results using CNN //Procedia Computer Science. – 2018. – Т. 136. – С. 321-330. View on GitHub

Vychuzhanin P., Nikitin N. O., Kalyuzhnaya A. V. Robust Ensemble-Based Evolutionary Calibration of the Numerical Wind Wave Model //International Conference on Computational Science. – Springer, Cham, 2019. – С. 614-627. View on GitHub

Social Media Study

Investigation of social media profiles and activities for personality prediction

Deeva I. Computational Personality Prediction Based on Digital Footprint of a Social Media User //Procedia Computer Science. – 2019. – Т. 156. – С. 185-193.

Kalyuzhnaya A. V. et al. Precedent-Based Approach for the Identification of Deviant Behavior in Social Media //International Conference on Computational Science. – Springer, Cham, 2018. – С. 846-852.

Uteuov A. Topic model for online communities’ interests prediction //Procedia Computer Science. – 2019. – Т. 156. – С. 204-213.

Weather Forecasting

Forecasting of natural accidents and weather conditions

Nikitin N. O. et al. Statistics-based models of flood-causing cyclones for the Baltic Sea region //Procedia Computer Science. – 2016. – Т. 101. – С. 272-281.

Noymanee J., Nikitin N. O., Kalyuzhnaya A. V. Urban Pluvial Flood Forecasting using Open Data with Machine Learning in Pattani Basin //Procedia computer science. – 2017. – Т. 119. – С. 288-297.

Uteuov A., Kalyuzhnaya A., Boukhanovsky A. The cities weather forecasting by crowdsourced atmospheric data //Procedia Computer Science. – 2019. – Т. 156. – С. 347-356.

Some of us

Member Contacts Main scientific projects
drawing Anna Kalyuzhnaya (anna.kalyuzhnaya@itmo.ru) Head of NSS Laboratory and Master ‘s Program “Digital Geotechnologies.” Probabilistic models of natural and social systems, generative methods AutoML
drawing Nikolay Nikitin (nnikitin@itmo.ru) AutoML and composite models, generative design of digital and physical objects, numerical optimization, environmental simulation
drawing Alexander Hvatov (alex_hvatov@itmo.ru) Evolutionary algorithms for data-driven modeling, differential equations, acoustics, etc.
drawing Irina Deeva (ideeva@itmo.ru) Bayesian networks, synthetic data generation, statistical analysis of multivariate data, social data modeling
drawing Mikhail Sarafanov (mik_sar@itmo.ru) Time series forecasting, software development of an AutoML framework, remote sensing data processing
drawing Julia Borisova (jul.borisova@itmo.ru) Time series processing and predictive modeling, hybridization and ensemble learning, geoinformatics tasks
drawing Ilya Revin (ierevin@itmo.ru) Machine learning methods for seismic exploration and oil reservoir engeeniring, environmental dynamical system modeling, time series analysis
drawing Gleb Maximov (maximovglebigorevich@gmail.com) Expanding the functionality of the framework. Composite neural networks for object recognition

And Mikhail Maslyaev, Anna Bubnova, Petr Andryuschenko, Anastasia Yashchenko, Mark Merezhnikov.

Towards Data Science articles

How AutoML helps to create composite AI?

AutoML for time series: definitely a good idea

AutoML for time series: advanced approaches with FEDOT framework

Habr articles

Как AutoML помогает создавать модели композитного ИИ — говорим о структурном обучении и фреймворке FEDOT

Прогнозирование временных рядов с помощью AutoML

Как мы “повернули реки вспять” на Emergency DataHack 2021, объединив гидрологию и AutoML

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