NSS Team

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Here are some of our projects:

AutoML

FEDOT, FEDOT.Industrial and GAMLET (Meta-AutoML).

Revin I. et al. Automated machine learning approach for time series classification pipelines using evolutionary optimisation //Knowledge-Based Systems. – 2023. – С. 110483.

Nikitin N. O. et al. Hybrid and automated machine learning approaches for oil fields development: The case study of Volve field, North Sea //Computers & Geosciences. – 2022. – Т. 161. – С. 105061.

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 // Advances in Intelligent Systems and Computing book series, volume 1401 – 2021.

Polonskaia I. S. et al. Multi-Objective Evolutionary Design of Composite Data-Driven Models // 2021 IEEE Congress on Evolutionary Computation (CEC) – 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.

Probabilistic modelling

BAMT - 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.

Bubnova A., Deeva I., Kalyuzhnaya A.V. MIxBN: library for learning Bayesian networks from mixed data // Procedia Computer Science - 2021, Vol. 193, pp. 494-503

Deeva I., Mossyayev A., Kalyuzhnaya A.V. A Multimodal Approach to Synthetic Personal Data Generation with Mixed Modelling: Bayesian Networks, GAN’s and Classification Models // Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering - 2022, Vol. 419, pp. 847-859

Bezborodov A., Deeva I. Networks clustering-based approach for search of reservoirs-analogues // Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - 2022, No. in press, pp. 1-7

Безбородов А.К., Деева И.Ю. Поиск месторождений-аналогов на основе кластеризации байесовских сетей // Известия высших учебных заведений. Приборостроение - 2022. - Т. 65. - № 1. - С. 64-72

Andriushchenko P. et al. Oil reservoir recovery factor assessment using Bayesian networks based on advanced approaches to analogues clustering //arXiv preprint arXiv:2204.00413. – 2022.

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.

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Equation discovery

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

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.

Generative design

GEFEST - toolbox for the generative design of physical objects and GOLEM - optimizer for DAGs.

Solovev G. V. et al. AI Framework for Generative Design of Computational Experiments with Structures in Physical Environment //NeurIPS 2023 AI for Science Workshop. – 2023.

Starodubcev N. O. et al. Generative design of physical objects using modular framework //Engineering Applications of Artificial Intelligence. – 2023.

Georgii V. Grigorev, Nikolay O. Nikitin et al. Single Red Blood Cell Hydrodynamic Traps Via the Generative Design // Micromachines, 2022

Nikitin N. O. et al. The multi-objective optimisation of breakwaters using evolutionary approach //Developments in Maritime Technology and Engineering. – CRC Press, 2021.

Starodubcev N. O., Nikitin N. O., Kalyuzhnaya A. V. Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks // 2022 IEEE Congress on Evolutionary Computation (CEC). – IEEE, 2022. – С. 1-8.

Nikitin N. O. et al. Generative design of microfluidic channel geometry using evolutionary approach//Proceedings of the Genetic and Evolutionary Computation Conference Companion. – 2021.

Metocean Simulation

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

Sarafanov M. et al. Short-Term River Flood Forecasting Using Composite Models and Automated Machine Learning: The Case Study of Lena River // Water. – 2021.

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 //Maritime Technology and Engineering 5 Volume 2. – CRC Press, 2021. – С. 767-774.

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

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
	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
	Nikolay Nikitin (nnikitin@itmo.ru)	AutoML and composite models, generative design of digital and physical objects, numerical optimization, environmental simulation
	Alexander Hvatov (alex_hvatov@itmo.ru)	Evolutionary algorithms for data-driven modeling, differential equations, acoustics, etc.
	Irina Deeva (ideeva@itmo.ru)	Bayesian networks, synthetic data generation, statistical analysis of multivariate data, social data modeling
	Julia Borisova (jul.borisova@itmo.ru)	Time series processing and predictive modeling, hybridization and ensemble learning, geoinformatics tasks
	Ilya Revin (ierevin@itmo.ru)	Machine learning methods for seismic exploration and oil reservoir engeeniring, environmental dynamical system modeling, time series analysis
	Grigorii Kirgizov (gvkirgizov@itmo.ru)	Evolutionary optimization, Graph optimization, AutoML, Reinforcement Learning

And Bashkova Ksenia, Ivanchik Elizaveta, Kuptsov Ilya, Kuznetsov Andrey, Lobanov Ivan, Mardyshkin Rostislav, Markov Ilya, Egor Shikov, Mikhail Maslyaev, Yaroslav Aksenkin, Yuri Kaminsky, Valery Pokrovsky, Nikita Balabanov, Maiia Pinchuk, Elizaveta Lucenko, Roman Netrogolov, Peter Shevchenko, Julia Shvarcberg, Andrey Getmanov, Stebenkov Andrey, Lyubov Yamshchikova, Karine Shakhkyan, Potemkin Vadim, Titov Roman, Gleb Soloviev, Maksimenko Artem, Petrov Oleg, Rubin Ivan, Sokolov Ilya, Elena Ilinskaya.