Научные руководители
Никитин Николай Олеговичкандидат технических наук nnikitin@itmo.ru Структурное подразделение: исследовательский центр в сфере искусственного интеллекта "Сильный искусственный интеллект в промышленности" Должность: руководитель группы научно-технического развития Структурное подразделение: лаборатория композитного искусственного интеллекта 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 |
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 |
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 |
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 |
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., 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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |