An LLM-Powered Tool for Enhancing Scientific Open-Source Repositories // Championing Open-source DEvelopment in ML Workshop @ ICML25
Соловьев Г.В., Nikitin N.O., Соколов М.С., Khussein A. Augmentation of Laser Welding Dataset through a combination of Evolutionary Optimization and Deep Learning. Proceedings of the Genetic and Evolutionary Computation Conference Companion.. 2025. pp. 2.
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.
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.
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.
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.
Generative AI for Co-Crystal Design with Property Control
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.
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.
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.
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.
Borisova J., Nikitin N. Lightweight Neural Ensemble Approach for Arctic Sea Ice Forecasting. IEEE Congress on Evolutionary Computation, CEC 2024. 2024. pp. 1-8.
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.
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.
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.
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.
Современные методы оптимизации с примерами на Python
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Borisova J., Aladina A., Nikitin N.O. Hybrid Modelling of Environmental Processes using Composite Models. Procedia Computer Science. 2021. Vol. 193. pp. 256-265.
Применение методов автоматического машинного обучения для прогнозирования временных рядов
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.
Оценка чувствительности композитных моделей в рамках фреймворка автоматического машинного обучения
Платформа интерактивного построения композитных моделей на основе автоматического машинного обучения
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.
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.
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.
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.
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.
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.
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.
Интерактивный анализ и визуализация процессов идентификации композитных моделей
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.
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.
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.
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.
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.
Применение методов машинного обучения для заполнения пропусков в данных дистанционного зондирования
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.
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.
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.
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.
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.
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.
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.
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.
Эволюционный подход к управлению качеством ансамблевых моделей
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.
Никитин Н.О. Программный комплекс для моделирования синтетических циклонов. Свидетельство о регистрации программы для ЭВМ. 2017. Т. 2017616937. № от 20.06.2017.
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.
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.
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Греция, Ираклион
Российская Федерация, Санкт-Петербург