Научные интересы

Информатика, сетевые технологии, инфокоммуникационные технологии и вычислительные системы.

Научные публикации

•‎ A.P. Kuleshov, A.V. Bernstein. Extended Regression on Manifolds Estimation. Lecture Notes in Artificial Intelligence, vol. 9653 ‘Conformal and Probabilistic Prediction with Applications’, Heidelberg, Springer, pp. 208–228, 2016.
•‎ A.P. Kuleshov, A.V. Bernstein. “Statistical Learning on Manifold-valued Data,” Lecture Notes in Artificial Intelligence, vol. 9729 ‘Machine Learning and Data Mining in Pattern Recognition,’ Switzerland, Springer International Publishing, pp. 311–325, 2016.
•‎ Kuleshov A., Bernstein A. Incremental Construction of Low-Dimensional Data Representations. Lecture Notes in Artificial Intelligence, vol. 9896 ‘Artificial Neural Networks for Pattern Recognition’, pp. 55–67, Springer Heidelberg, 2016.
•‎ Kuleshov A., Bernstein A. Nonlinear multi-output regression on unknown input manifold. Annals of Mathematics and Artificial Intelligence, vol. 81, №1-2, pp. 209-240, 2017.
•‎ Kuleshov A., Bernstein A., Burnaev A. Mobile Robot Localization via Machine Learning. Lecture Notes in Artificial Intelligence, vol. 10358 “Machine Learning and Data Mining in Pattern Recognition”, pp. 276-290, Springer International Publishing AG, Verlag, 2017.
•‎ Kuleshov A., Bernstein A., Burnaev E., Yanovich, Yu. Machine Learning in Appearance-based Robot Self-localization. Proceedings of the 16th International IEEE Conference on Machine Learning and Applications (ICMLA-2017), IEEE Computer Society, pp. 106-112, 2017.
•‎ A.P. Kuleshov, A.V. Bernstein, E.V. Burnaev. Kernel Regression on Manifold Valued Data. Proceedings of the 5th International IEEE Conference on Data Science and Advanced Analytics (DSAA 2018), IEEE Computer Society, 120-129, 2018.
•‎ Kuleshov A., Bernstein A., Burnaev. E. Conformal Prediction in Manifold Learning. Proceedings of Machine Learning Research, vol. 91 (PMLR 91) “Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications”, pp. 234-253, 2018.
•‎ A.P. Kuleshov, A.V. Bernstein, E.V. Burnaev. “Manifold learning regression with non-stationary kernels,” Lecture Notes in Artificial Intelligence Series, vol. 11081 ‘Artificial Neural Networks for Pattern Recognition,’ Springer Nature International Switzerland, pp. 152–164, 2018.
•‎ Andrzej Cichocki, Alexander P. Kuleshov, Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles, Computational Intelligence and Neuroscience, 2021.

Премии и награды

•‎ Медаль «За трудовую доблесть».
•‎ Звание Офицера Национального Ордена Франции «За заслуги».

Персональные профили исследователя

•‎ Web of Science: L-7275-2015.
•‎ Scopus: 25621485600.
•‎ ORCID: 0000-0001-9736-2929.
•‎ MathSciNet: 47246.