Никитин Николай Олегович

Статья

Solovev G., Sokolov M., Hussein A., Nikitin N. Augmentation of Laser Welding Dataset through a combination of Evolutionary Optimization and Deep Learning. GECCO 2025 - Proceedings of the Genetic and Evolutionary Computation Conference Companion. 2025. pp. 113-114.. doi: 10.1145/3712255.3734268

Статья

Borisova J., Nikitin N. Towards importance of periodicity estimation in long-term spatio-temporal data prediction. 2025 International Joint Conference on Neural Networks (IJCNN). 2025. pp. 1-8.. doi: 10.1109/IJCNN64981.2025.11228079

Статья

Gilemkhanov D., Solovev G.V., Zhidkovskaya A.B., Orlova A., Gubina N., Vepreva A., Golovinskii R., Tonkii I., Dubrovsky I., Gurev I., Chistiakov D., Aliev T., Poddiakov I., Zubkova G., Skorb E.V., Vinogradov V., Boukhanovsky A., Nikitin N.O., Dmitrenko A., Kalyuzhnaya A.V., Savchenko A. MADD: Multi-Agent Drug Discovery Orchestra. Findings of the Association for Computational Linguistics: EMNLP 2025. 2025. pp. 6956–6998.. doi: 10.18653/v1/2025.findings-emnlp.367

Статья

Lunev A., Nikitin N. Neuron-Level Architecture Search for Efficient Model Design. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2025. Vol. 16090. pp. 191-203.. doi: 10.1007/978-3-032-05461-6_13

Статья

Francisco T.J., Da Silva Macedo B., Yaseen Z.M., Nikitin N.O., Bodini M., Gorgoglione A., Saporetti C., Goliatt L. Evolutionary polynomial modeling for interpretable drought prediction and resilient resource management. Ecological Informatics. 2025. Vol. 90. pp. 103217.. doi: 10.1016/j.ecoinf.2025.103217

Статья

Kalyuzhnaya A., Mityagin S., Lutsenko E., Getmanov A., Aksenkin Y., Fatkhiev K., Fedorin K., Nikitin N.O., Chichkova N., Vorona V., Boukhanovsky A. LLM Agents for Smart City Management: Enhancing Decision Support Through Multi-Agent AI Systems. Smart Cities. 2025. Vol. 8. No. 1. pp. 19.. doi: 10.3390/smartcities8010019

Статья

Borisova J., Morshchinin I.V., Nazarova V.I., Molodkina N., Nikitin N.O. Low-Cost Microalgae Cell Concentration Estimation in Hydrochemistry Applications Using Computer Vision. Sensors. 2025. Vol. 25. No. 15. pp. 4651.. doi: 10.3390/s25154651

Статья

Никитин Н.О., Борисова Ю.И., Аксенкин Я.В., Башкова К., Луценко Е.И., Калюжная А.В., Якимушкин Д.О., Котилевская А.М., Верташ Т.Н., Колюбакин А.А., Багорьян Е.С., Бухановский А.В. Предвычисление ледовых условий для обеспечения хозяйственной деятельности в морях российской Арктики с помощью методов глубокого обучения [Prediction of ice conditions to support economic activity in the Russian Arctic seas using deep learning methods]. Арктика: экология и экономика [Arktika: Ekologia i Ekonomika]. 2025. Т. 15. № 1(57). С. 119-130.. doi: 10.25283/2223-4594-2025-1-119-130

Статья

Borisova J., Kuznetsov A., Solovev G., Nikitin N.O. Understanding the Limitations of Deep Transformer Models for Sea Ice Forecasting. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2025. Vol. 15905. pp. 104-118.. doi: 10.1007/978-3-031-97632-2_8

Тезисы

An LLM-Powered Tool for Enhancing Scientific Open-Source Repositories // Championing Open-source DEvelopment in ML Workshop @ ICML25

Статья

Kirgizov G., Nikitin N.O., Pinchuk M., Yamshchikova L., Deeva I., Shakhkyan K., Borisov I.I., Zharkov K.D., Kalyuzhnaya A.V. Automated Design of Graph-based Models and Structures using Modular Evolutionary Framework. 4th workshop on Graphs and more Complex structures for Learning and Reasoning (GCLR 2024). Colocated with AAAI 2024. 2024. pp. accepted-papers.

Статья

Sidorenko D., Starodubcev N., Pinchuk M., Nikitin N.O. Interpretable Structural Analysis for Evolutionary Generative Design of Coastal Breakwaters. Communications in Computer and Information Science. 2024. Vol. 1981. pp. 172-185.. doi: 10.1007/978-3-031-53025-8_13

Статья

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. 2024. Vol. 302. pp. 112363.. doi: 10.1016/j.knosys.2024.112363

Тезисы

Generative AI for Co-Crystal Design with Property Control

Статья

Solovev G.V., Hvatov A., Petrov O., Kalyuzhnaya A., Klimova A., Nikitin N.O. Evolutionary Optimization for Inverse Problem in Engineering: The Case Study of Defects Shape Reconstruction. Communications in Computer and Information Science. 2024. Vol. 2281. pp. 125-140.. doi: 10.1007/978-3-031-77432-4_9

Статья

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.. doi: 10.1109/CEC60901.2024.10611825

Статья

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.. doi: 10.1145/3638530.3664141

Статья

Иов И.Л., Никитин Н.О. Feature Engineering Pipeline Optimization in AutoML Workflow Using Large Language Models. Proceedings of Artificial Intelligence and Natural Language AINL. 2024. pp. 0.

Статья

Borisova J., Nikitin N. Lightweight Neural Ensemble Approach for Arctic Sea Ice Forecasting. IEEE Congress on Evolutionary Computation, CEC 2024. 2024. pp. 1-8.. doi: 10.1109/CEC60901.2024.10612104

Статья

Gubina N., Dmitrenko A., Solovev G., Yamshchikova L., Petrov O., Lebedev I., Serov N., Kirgizov G., Nikitin N., Vinogradov V. Hybrid Generative AI for De Novo Design of Co-Crystals with Enhanced Tabletability. Advances in Neural Information Processing Systems. 2024. Vol. 37. pp. 1-39.

Статья

Иов И.Л., Никитин Н.О. Feature engineering pipeline optimisation in AutoML workflow using large language models [Оптимизация конвейера разработки признаков в AutoML с использованием крупных языковых моделей]. Записки научных семинаров Санкт-Петербургского отделения Математического института им. В.А.Стеклова РАН. 2024. Т. 540. С. 82-112.

Статья

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.. doi: 10.1016/j.engappai.2022.105715

Статья

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.. doi: 10.1016/j.knosys.2023.110483

Статья

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.. doi: 10.1016/j.procs.2023.12.001

Статья

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.. doi: 10.1109/CEC53210.2023.10254012

Статья

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.. doi: 10.1016/j.procs.2023.12.009

Учебник, учебное пособие

Современные методы оптимизации с примерами на Python

Статья

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.. doi: 10.1109/CEC55065.2022.9870347

Статья

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.. doi: 10.1007/978-3-030-87869-6_60

Статья

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.. doi: 10.1016/j.cageo.2022.105061

Статья

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.. doi: 10.1016/j.future.2021.08.022

Статья

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.. doi: 10.1109/CEC55065.2022.9870336

Тезисы

Платформа интерактивного построения композитных моделей на основе автоматического машинного обучения

Тезисы

Применение методов автоматического машинного обучения для прогнозирования временных рядов

Тезисы

Интерактивный анализ и визуализация процессов идентификации композитных моделей

Статья

Borisova J., Aladina A., Nikitin N.O. Hybrid Modelling of Environmental Processes using Composite Models. Procedia Computer Science. 2021. Vol. 193. pp. 256-265.. doi: 10.1016/j.procs.2021.10.026

Статья

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.. doi: 10.1016/j.procs.2021.10.050

Статья

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.. doi: 10.1016/j.procs.2021.10.021

Статья

Nikitin N.O., Vychuzhanin P., Sarafanov M., Polonskaia I.S., Kaluzhnaya A.V. Multi-Purpose Evolutionary AutoML for the Generative Design of Composite Modelling Pipelines. KDD-AutoML Workshop 2021. 2021. pp. 1-6.

Статья

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.. doi: 10.1201/9781003216599-82

Статья

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.. doi: 10.1145/3449726.3462740

Тезисы

Оценка чувствительности композитных моделей в рамках фреймворка автоматического машинного обучения

Статья

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.. doi: 10.3390/e23010028

Статья

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.. doi: 10.1007/978-3-030-91885-9_6

Статья

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.. doi: 10.1007/978-3-030-77961-0_33

Статья

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.. doi: 10.3390/w13243482

Статья

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.. doi: 10.1109/CEC45853.2021.9504773

Статья

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.. doi: 10.3390/rs12233865

Статья

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.

Статья

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.

Статья

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.. doi: 10.1145/3377929.3398167

Статья

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.. doi: 10.1016/j.procs.2020.11.043

Тезисы

Применение методов машинного обучения для заполнения пропусков в данных дистанционного зондирования

Статья

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.. doi: 10.1016/j.procs.2019.08.212

Статья

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.. doi: 10.1145/3319619.3326876

Статья

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.. doi: 10.1016/j.ocemod.2019.101427

Статья

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.. doi: 10.1007/978-3-030-22734-0_45

Статья

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.. doi: 10.1155/2018/5870987

Статья

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.. doi: 10.1145/3205651.3205751

Статья

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.. doi: 10.1007/978-3-319-93713-7_81

Статья

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.. doi: 10.1016/j.procs.2018.08.283

Статья

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.. doi: 10.1007/978-3-319-93713-7_84

Тезисы

Эволюционный подход к управлению качеством ансамблевых моделей

Статья

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.. doi: 10.1016/j.procs.2017.11.187

Статья

Никитин Н.О. Программный комплекс для моделирования синтетических циклонов. Свидетельство о регистрации программы для ЭВМ. 2017. Т. 2017616937. № от 20.06.2017.

Статья

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.. doi: 10.1016/j.procs.2016.11.032

Тезисы

Использование индексирования для аннотирования документов