Научные руководители
 |
Калюжная Анна Владимировнакандидат технических наук anna.kalyuzhnaya@itmo.ru Структурное подразделение: лаборатория композитного искусственного интеллекта 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: 05.13.17 - Теоретические основы информатики 25.00.35 - Геоинформатика 05.13.18 - Математическое моделирование, численные методы и комплексы программ 2.3.8. - Информатика и информационные процессы 1.2.1. - Искусственный интеллект и машинное обучение 1.2.2. - Математическое моделирование, численные методы и комплексы программ 1.2.3. - Теоретическая информатика, кибернетика 1.2.1 - Искусственный интеллект и машинное обучение Область интересов: Математическое моделирование, многомерный статистический анализ, анализ временных рядов, модели на данных, интеллектуальный анализ данных, машинное обучение. Рабочий язык: Английский, Русский |
Публикации руководителя
| Выходные данные | Год | Индексирование в БД |
| 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 |
| 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 |
| Deeva I., Kalyuzhnaya A.V., Boukhanovsky A.V. Adaptive Learning Algorithm for Bayesian Networks Based on Kernel Mixtures Distributions//International Journal of Artificial Intelligence, 2023, Vol. 21, No. 1, pp. 90-108 | 2023 | Scopus, Белый список |
| 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 |
| Filatova A., Kovalchuk M., Batalenkov S., Voskresenskiy A., Deeva I., Kalyuzhnaya A., Shpilman A., Kondrashova N., Dudnichenko M., Nasonov D. A Multi-Contractor Approach for MLRCPSP with the Graph Structure Optimization//IEEE Congress on Evolutionary Computation, CEC 2023, 2023, pp. 1-8 | 2023 | Scopus, Web of Science |
| Nizovtseva I., Palmin V., Simkin I., Starodumov I., Mikushin P., Nozik A., Hamitov T., Ivanov S., Vikharev S., Zinovev A., Svitich V., Mogilev M., Nikishina M., Kraev S., Yurchenko S., Mityashin T., Chernushkin D., Kalyuzhnaya A., Blyakhman F. Assessing the Mass Transfer Coefficient in Jet Bioreactors with Classical Computer Vision Methods and Neural Networks Algorithms//Algorithms, 2023, Vol. 16, No. 3, pp. 125 | 2023 | Scopus, Web of Science, Белый список |
| 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 |
| Deeva I., Bubnova A., Kalyuzhnaya A.V. Advanced Approach for Distributions Parameters Learning in Bayesian Networks with Gaussian Mixture Models and Discriminative Models//Mathematics, 2023, Vol. 11, No. 2, pp. 343 | 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 |
| 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 | 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 |
| 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 |
| Быков Н.Ю., Хватов А.А., Калюжная А.В., Бухановский А.В. Метод восстановления моделей тепломассопереноса по пространственно-временным распределениям параметров // Письма в Журнал технической физики -2021. - Т. 47. - № 24. - С. 9-12 | 2021 | RSCI, ВАК, РИНЦ |
| 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 |
| Bykov N., Hvatov A., Kalyuzhnaya A.V., Boukhanovsky A.V. A method of generative model design based on irregular data in application to heat transfer problems//Journal of Physics: Conference Series, 2021, Vol. 1959, No. 1, pp. 012012 | 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 |
| 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 |
| 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 | 2021 | Scopus, Web of Science |
| Maslyaev M., Hvatov A., Kalyuzhnaya A.V. Partial differential equations discovery with EPDE framework: Application for real and synthetic data (R)//Journal of Computational Science, 2021, Vol. 53, pp. 101345 | 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 |
| 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 |
| Андрющенко П.Д., Деева И.Ю., Калюжная А.В., Бубнова А.В., Воскресенский А.Г., Буханов Н.В. Анализ параметров нефтегазовых месторождений с использованием байесовских сетей [Analysis of parameters of oil and gas fields using Bayesian networks] // Интеллектуальный анализ данных в нефтегазовой отрасли: сборник тезисов конференции [Data Science in Oil and Gas 2020] -2020. - С. 1-10 | 2020 | Scopus, РИНЦ |
| Maslyaev M., Hvatov A., Kalyuzhnaya A.V. Discovery of the data-driven models of continuous metocean process in form of nonlinear ordinary differential equations//Procedia Computer Science, 2020, Vol. 178, pp. 18-26 | 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 |
| 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 | 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 |
| 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 |
| Maslyaev M., Hvatov A., Kalyuzhnaya A. Data-Driven Partial Differential Equations Discovery Approach for the Noised Multi-dimensional Data//Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020, Vol. 12138 LNCS, pp. 86-100 | 2020 | Scopus, Web of Science |
| Uteuov A., Kalyuzhnaya A.V., Boukhanovsky A.V. The cities weather forecasting by crowdsourced atmospheric data//Procedia Computer Science, 2019, Vol. 156, pp. 347-356 | 2019 | Scopus, Web of Science |
| Maslyaev M., Hvatov A., Kalyuzhnaya A.V. Data-driven partial derivative equations discovery with evolutionary approach//Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, Vol. 11540 LNCS, pp. 635-641 | 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 |
| Вычужанин П.В., Калюжная А.В. Робастная калибровка параметров численной модели ветрового волнения SWAN // Альманах научных работ молодых ученых Университета ИТМО -2019. - Т. 3. - С. 151-155 | 2019 | 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 |
| 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 |
| 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 |
| Uteuov A., Kalyuzhnaya A. Combined document embedding and hierarchical topic model for social media texts analysis//Procedia Computer Science, 2018, Vol. 136, pp. 293-303 | 2018 | Scopus, Web of Science |
| Вычужанин П.В., Калюжная А.В. Разработка системы автоматизированной верификации гидрометеорологической вычислительной системы // Альманах научных работ молодых ученых Университета ИТМО -2018. - Т. 2. - С. 114-117 | 2018 | РИНЦ |
| Kalyuzhnaya A.V., Nasonov D., Ivanov S.V., Kosukhin S.S., Boukhanovsky A.V. Towards a scenario-based solution for extreme metocean event simulation applying urgent computing//Future Generation Computer Systems, 2018, Vol. 79, No. Part.2, pp. 604-617 | 2018 | 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 |
| 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 |
| Утеуов А.К., Арайа Лопес Х., Калюжная А.В. Контроль качества и восстановление пропусков в гидрометеорологических данных // Альманах научных работ молодых ученых Университета ИТМО -2018. - Т. 2. - С. 141-144 | 2018 | РИНЦ |
| Vychuzhanin P., Hvatov A., Kalyuzhnaya A.V. Anomalies Detection in Metocean Simulation Results Using Convolutional Neural Networks//Procedia Computer Science, 2018, Vol. 136, pp. 321-330 | 2018 | Scopus, Web of Science |
| Nikishova A.V., Kalyuzhnaya A.V., Boukhanovsky A.V., Khukstra A. Uncertainty quantification and sensitivity analysis applied to the wind wave model SWAN//Environmental Modelling and Software, 2017, Vol. 95, pp. 344-357 | 2017 | Scopus, Web of Science |
| Lopez J.L., Uteuov A., Kalyuzhnaya A.V. Quality control and data restoration of metocean Arctic data//Procedia Computer Science, 2017, Vol. 119, pp. 315-324 | 2017 | 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 |
| Gusarov A., Kalyuzhnaya A.V., Boukhanovsky A.V. Spatially adaptive ensemble optimal interpolation of in-situ observations into numerical vector field models//Procedia Computer Science, 2017, Vol. 119, pp. 325-333 | 2017 | Scopus, Web of Science |
| Araya-Lopez J., Kaluzhnaya A.V., Kosukhin S.S., Ivanov S.V. Data Quality Control for St. Petersburg flood warning system//Procedia Computer Science, 2016, Vol. 80, pp. 2128-2140 | 2016 | 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 |
| Kaluzhnaya A.V., Boukhanovsky A.V. Computational uncertainty management for coastal flood prevention system//Procedia Computer Science, 2015, Vol. 51, pp. 2317-2326 | 2015 | Scopus, Web of Science |
| Kosukhin S.S., Kaluzhnaya A.V., Nikishova A.V., Boukhanovsky A.V. Special aspects of wind wave simulations for surge flood forecasting and prevention//Procedia Computer Science, 2015, Vol. 66, pp. 184-190 | 2015 | Scopus, Web of Science |
| Kalyuzhnaya A.V., Visheratin A.A., Dudko A., Nasonov D.A., Boukhanovsky A.V. Synthetic storms reconstruction for coastal floods risks assessment//Journal of Computational Science, 2015, Vol. 9, pp. 112-117 | 2015 | Scopus, Web of Science |
| Visheratin A.A., Nasonov D.A. ., Kaluzhnaya, A.V. ., Kosukhin, S.S. . A simulation platform for atmospheric phenomena study within coastal floods in Baltic sea area//International Multidisciplinary Scientific GeoConference-SGEM: 15th International Multidisciplinary Scientific Geoconference SGEM 2015, 2015, Vol. 1, No. 2, pp. 11-18 | 2015 | Scopus, Web of Science |
| Kosukhin, S.S. ., Kaluzhnaya, A.V. ., Nasonov D. Problem solving environment for development and maintenance of St. Petersburg’s Flood Warning System//Procedia Computer Science, 2014, Vol. 29, pp. 1667–1676 | 2014 | Scopus, Web of Science |
| Kaluzhnaya A.V., Nasonov D.A., Boukhanovsky A.V. . Ensemble risk assessment for flood warning system in st. Petersburg//14th International Multidisciplinary Scientific Geoconference SGEM 2014. GeoConference on Informatics, Geoinformatics and Remote Sensing. Conference Proceedings, 2014, Vol. 1, No. 3, pp. 247-256 | 2014 | Scopus, Web of Science |
| Ivanov, S.V. ., Kosukhin, S.S. ., Kaluzhnaya, A.V. ., Boukhanovsky, A.V. . Erratum to Simulation-based collaborative decision support for surge floods prevention in St. Petersburg [J. Comput. Sci. 3 (2012) 450-455]//Journal of Computational Science, 2013, Vol. 4, No. 5, pp. 438 | 2013 | Scopus, Web of Science |
| Ivanov S.V., Kosukhin S.S., Kaluzhnaya A.V., Boukhanovsky A.V. Simulation-based collaborative decision support for surge floods prevention in St. Petersburg//Journal of Computational Science, 2012, Vol. 3, No. 6, pp. 450-455 | 2012 | Scopus, Web of Science |
| Мостаманди М.В., Насонов Д.А., Калюжная А.В., Бухановский А.В. Ансамблевые прогнозы экстремальных гидрометеорологических явлений в распределенной среде CLAVIRE // Известия высших учебных заведений. Приборостроение -2011. - Т. 54. - № 10. - С. 100-102 | 2011 | ВАК, РИНЦ |