Научные руководители
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Никитин Николай Олеговичкандидат технических наук nnikitin@itmo.ru Структурное подразделение: лаборатория композитного искусственного интеллекта Должность: руководитель группы научно-технического развития Структурное подразделение: национальный центр когнитивных разработок Должность: старший научный сотрудник Структурное подразделение: национальный центр когнитивных разработок Должность: старший научный сотрудник Структурное подразделение: факультет цифровых трансформаций Должность: доцент (квалификационная категория "ординарный доцент") Профиль: 2.3.5. - Математическое и программное обеспечение вычислительных систем, комплексов и компьютерных сетей 1.2.1 - Искусственный интеллект и машинное обучение 1.2.2. - Математическое моделирование, численные методы и комплексы программ 1.2.1. - Искусственный интеллект и машинное обучение Область интересов: Автоматическое машинное обучение, генеративный дизайн, композитный ИИ, эволюционная оптимизация. Рабочий язык: Русский |
Публикации руководителя
| Выходные данные | Год | Индексирование в БД |
| Getmanov A., Nikitin N.O. Evolutionary Automated Machine Learning for Light-Weight Multi-Modal Pipelines//IEEE Congress on Evolutionary Computation, CEC 2024, 2024, pp. 1-8 | 2024 | Scopus, Web of Science |
| Borisova J., Nikitin N. Lightweight Neural Ensemble Approach for Arctic Sea Ice Forecasting//IEEE Congress on Evolutionary Computation, CEC 2024, 2024, pp. 1-8 | 2024 | Scopus, Web of Science |
| Pinchuk M., Kirgizov G., Yamshchikova L., Nikitin N., Deeva I., Shakhkyan K., Borisov I., Zharkov K., Kalyuzhnaya A. GOLEM: Flexible Evolutionary Design of Graph Representations of Physical and Digital Objects//GECCO 2024 - Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2024, pp. 1668-1675 | 2024 | Scopus, Web of Science |
| Nikitin N.O., Teryoshkin S., Pokrovskii V., Pakulin S., Nasonov D. Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment//IEEE Congress on Evolutionary Computation, CEC 2023, 2023, pp. 1-8 | 2023 | Scopus, Web of Science |
| Starodubcev N., Nikitin N., Andronova E., Gavaza K., Sidorenko D., Kalyuzhnaya A.V. Generative design of physical objects using modular framework//Engineering Applications of Artificial Intelligence, 2023, Vol. 119, pp. 105715 | 2023 | Scopus, Web of Science, Белый список |
| Stebenkov A.S., Nikitin N.O. Automated Generation of Ensemble Pipelines using Policy-Based Reinforcement Learning method//Procedia Computer Science, 2023, Vol. 229, pp. 70-79 | 2023 | Scopus, Web of Science |
| Revin I., Potemkin V., Balabanov N., Nikitin N.O. Automated machine learning approach for time series classification pipelines using evolutionary optimization//Knowledge-Based Systems, 2023, Vol. 268, pp. 110483 | 2023 | Scopus, Web of Science, Белый список |
| Klimova A., Nasonov D., Hvatov A., Nikitin N.O., Ivanov S.V., Kalyuzhnaya A.V., Boukhanovsky A. Strategic Trends in Artificial Intelligence Through Impact of Computational Science: What Young Scientists Should Expect//Procedia Computer Science, 2023, Vol. 229, pp. 1-7 | 2023 | Scopus, Web of Science |
| Nikitin N.O., Pinchuk M., Pokrovskii V., Shevchenko P., Getmanov A., Aksenkin Y., Revin I., Stebenkov A., Latypov V., Poslavskaya E., Kalyuzhnaya A.V. Integration Of Evolutionary Automated Machine Learning With Structural Sensitivity Analysis For Composite Pipelines//Knowledge-Based Systems, 2023, Vol. 302, pp. 112363 | 2023 | Scopus, Web of Science, Белый список |
| Starodubcev N., Nikitin N.O., Kalyuzhnaya A.V. Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks//IEEE Congress on Evolutionary Computation, CEC 2022, 2022, pp. 1-8 | 2022 | Scopus, Web of Science |
| Nikitin N.O., Vychuzhanin P., Sarafanov M., Polonskaia I.S., Revin I., Barabanova I.V., Kaluzhnaya A.V., Boukhanovsky A. Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelines//Future Generation Computer Systems, 2022, Vol. 127, pp. 109-125 | 2022 | Scopus, Web of Science |
| 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, 2022, Vol. 1401, pp. 633-642 | 2022 | Scopus, Web of Science |
| Sarafanov M., Pokrovskii V., Nikitin N.O. Evolutionary Automated Machine Learning for Multi-Scale Decomposition and Forecasting of Sensor Time Series//IEEE Congress on Evolutionary Computation, CEC 2022, 2022, pp. 1-8 | 2022 | Scopus, Web of Science |
| Nikitin N.O., Revin I., Hvatov A., Vychuzhanin P., Kalyuzhnaya A.V. Hybrid and Automated Machine Learning Approaches for Oil Fields Development: the Case Study of Volve Field, North Sea//Computers and Geosciences, 2022, Vol. 161, pp. 105061 | 2022 | Scopus, Web of Science |
| Deeva I., Bubnova A., Andriushchenko P.D., Voskresenskiy A., Bukhanov N.V., Nikitin N.O., Kalyuzhnaya A.V. Oil and Gas Reservoirs Parameters Analysis Using Mixed Learning of Bayesian Networks//Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021, Vol. 12742, pp. 394-407 | 2021 | Scopus, Web of Science |
| Kalyuzhnaya A.V., Nikitin N.O., Hvatov A., Maslyaev M., Yachmenkov M., Boukhanovsky A.V. Towards generative design of computationally efficient mathematical models with evolutionary learning//Entropy, 2021, Vol. 23, No. 1, pp. 28 | 2021 | Scopus, Web of Science |
| Hvatov A., Maslyaev M., Polonskaia I.S., Sarafanov M.I., Merezhnikov M., Nikitin N.O. Model-Agnostic Multi-objective Approach for the Evolutionary Discovery of Mathematical Models//Communications in Computer and Information Science, 2021, Vol. 1488, pp. 72-85 | 2021 | Scopus, Web of Science |
| Borisova J., Aladina A., Nikitin N.O. Hybrid Modelling of Environmental Processes using Composite Models//Procedia Computer Science, 2021, Vol. 193, pp. 256-265 | 2021 | Scopus, Web of Science |
| Sarafanov M.I., Borisova Y., Maslyaev M., Revin I., Maximov G., Nikitin N.O. Short-Term River Flood Forecasting Using Composite Models and Automated Machine Learning: The Case Study of Lena River//Water, 2021, Vol. 13, No. 24, pp. 3482 | 2021 | Scopus, Web of Science |
| Nikitin N.O., Hvatov A., Polonskaia I.S., Kalyuzhnaya A.V., Grigorev G., Wang X., Qian X. Generative design of microfluidic channel geometry using evolutionary approach//GECCO 2021 - Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021, pp. 59-60 | 2021 | Scopus, Web of Science |
| Barabanova I.V., Vychuzhanin P., Nikitin N.O. Sensitivity Analysis of the Composite Data-Driven Pipelines in the Automated Machine Learning//Procedia Computer Science, 2021, Vol. 193, pp. 484-493 | 2021 | Scopus, Web of Science |
| Polonskaia I.S., Nikitin N.O., Revin I., Vychuzhanin P., Kaluzhnaya A.V. Multi-Objective Evolutionary Design of Composite Data-Driven Models//IEEE Congress on Evolutionary Computation, CEC 2021, 2021, pp. 926-933 | 2021 | Scopus, Web of Science |
| Polonskaia I.S., Aliev I.R., Nikitin N.O. Automated Evolutionary Design of CNN Classifiers for Object Recognition on Satellite Images//Procedia Computer Science, 2021, Vol. 193, pp. 210-219 | 2021 | Scopus, Web of Science |
| Nikitin N.O., Polonskaia I.S., Kalyuzhnaya A.V., Boukhanovsky A.V. The multi-objective optimisation of breakwaters using evolutionary approach//Proceedings of the 5th International Conference on Maritime Technology and Engineering, MARTECH 2020, 2021, Vol. 2, pp. 767-774 | 2021 | Scopus |
| Kaluzhnaya A.V., Nikitin N.O., Vychuzhanin P., Hvatov A., Boukhanovsky A.V. Automatic Evolutionary Learning of Composite Models With Knowledge Enrichment//GECCO 2020 - Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2020, pp. 43-44 | 2020 | Scopus, Web of Science |
| Sarafanov M., Kazakov E.E., Nikitin N.O., Kalyuzhnaya A.V. 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, Vol. 12, No. 23, pp. 3865 | 2020 | Scopus, Web of Science |
| Калюжная А.В., Никитин Н.О., Вычужанин П.В., Хватов А.А. Технологии прикладного искусcтвенного интеллекта в задачах численного моделирования процессов в океане // Комплексные исследования Мирового океана: материалы V Всероссийской научной конференции молодых ученых (Калининград, 18-22мая 2020г.) -2020. - С. 81-82 | 2020 | РИНЦ |
| Nikitin N.O., Polonskaia I.S., Vychuzhanin P., Barabanova I.V., Kaluzhnaya A.V. Structural Evolutionary Learning for Composite Classification Models//Procedia Computer Science, 2020, Vol. 178, pp. 414-423 | 2020 | Scopus, Web of Science |
| Никитин Н.О., Полонская Я.С., Калюжная А.В. Интеллектуальное проектирование защитных сооружений на шельфе с применением моделей морской среды и методов оптимизации // Комплексные исследования Мирового океана: материалы V Всероссийской научной конференции молодых ученых (Калининград, 18-22мая 2020г.) -2020. - С. 141-142 | 2020 | РИНЦ |
| Deeva I., Nikitin N.O., Kalyuzhnaya A.V. Pattern Recognition in Non-Stationary Environmental Time Series Using Sparse Regression//Procedia Computer Science, 2019, Vol. 156, pp. 357-366 | 2019 | Scopus, Web of Science |
| Khvatov A.A., Nikitin N., Kaluzhnaya A.V., Kosukhin S.S. Adaptation of NEMO-LIM3 model for multigrid high-resolution Arctic simulation//Ocean Modelling, 2019, Vol. 141, pp. 101427 | 2019 | Scopus, Web of Science |
| Nikitin N.O., Deeva I., Vychuzhanin P., Kalyuzhnaya A.V., Hvatov A., Kovalchuk S.V. Deadline-driven approach for multi-fidelity surrogate-assisted environmental model calibration: SWAN wind wave model case study//GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion, 2019, pp. 1583-1591 | 2019 | Scopus, Web of Science |
| Vychuzhanin P., Nikitin N.O., Kalyuzhnaya A.V. Robust Ensemble-Based Evolutionary Calibration of the Numerical Wind Wave Model//Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, Vol. 11536, pp. 614-627 | 2019 | Scopus, Web of Science |
| Kovalchuk S.V., Kisliakovskii I.O., Metsker O.G., Nikitin N.O., Funkner A.A., Kalyuzhnaya A.V., Vaganov D.A., Bochenina K.O. Towards management of complex modeling through a hybrid evolutionary identification//GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion, 2018, pp. 255-256 | 2018 | Scopus |
| Kalyuzhnaya A.V., Nikitin N.O., Butakov N.A., Nasonov D.A. Precedent-based approach for the identification of deviant behavior in social media//Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, Vol. 10862, pp. 846-852 | 2018 | Scopus, Web of Science |
| Araya-Lopez J., Nikitin N.O., Kalyuzhnaya A.V. Case-adaptive ensemble technique for met-ocean data restoration//Procedia Computer Science, 2018, Vol. 136, pp. 311-320 | 2018 | Scopus, Web of Science |
| Nikitin N.O., Kalyuzhnaya A.V., Bochenina K., Kudryashov A., Uteuov A., Derevitskii I., Boukhanovsky A.V. Evolutionary ensemble approach for behavioral credit scoring//Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, Vol. 10862, pp. 825-831 | 2018 | Scopus, Web of Science |
| Kovalchuk S.V. ., Metsker O.G., Funkner A.A., Kisliakovskii I.O., Nikitin N.O., Kalyuzhnaya A.V., Vaganov D.A., Bochenina K.O. A Conceptual Approach to Complex Model Management with Generalized Modelling Patterns and Evolutionary Identification//Complexity, 2018, pp. 5870987 | 2018 | Scopus, Web of Science |
| Noymanee J., Nikitin N.O., Kaluzhnaya A.V. Urban Pluvial Flood Forecasting using Open Data with Machine Learning Techniques in Pattani Basin//Procedia Computer Science, 2017, Vol. 119, pp. 288-297 | 2017 | Scopus, Web of Science |
| Nikitin N.O., Spirin D.S., Visheratin A.A., Kalyuzhnaya A.V. Statistics-based models of flood-causing cyclones for the Baltic Sea region//Procedia Computer Science, 2016, Vol. 101, pp. 272–281 | 2016 | Scopus, Web of Science |