alexanderbernstein

Alexander Bernstein

Professor of the Practice
Research Center in Artificial Intelligence in the Direction of Optimization of Management Decisions to Reduce Carbon Footprint
  • Master degree in Mathematics, Department of Mechanics and Mathematics of Moscow State University, Moscow, Russia (1969)
  • PhD (Kandidat Nauk) in Physics and Mathematics from the Steklov Mathematical Institute (Leningrad Branch) of the USSR Academy of Science, Russia (1973)
  • Doctor of Sciences in Physics and Mathematics from the Department of Computational Mathematics and Cybernetics of the Moscow State University, Russia (1987)
  • Professor in the field of Intelligent Technologies and Systems (an academic rank) awarded by the USSR Higher Attestation Commission (1991)

Professor at the Practice Alexander Bernstein works at the Skoltech Center for Computational and Data-Intensive Science and Engineering since May 2016. He received a master degree in Math (1969) at the Department of Mechanics and Mathematics, Moscow State University, a PhD degree in Math (1973) from the Steklov Mathematical Institute of the USSR Academy of Sciences, and a Doctor of Sciences degree in Math (1987) from the Department of Computational Mathematics and Cybernetics, Moscow State University. In 1991, the USSR Higher Attestation Commission awarded Alexander with the academic rank of Professor in the field of Intelligent Technologies and Systems.

Prof. Bernstein started his career at the Research Institute of Automatic Equipment in 1969, where he was developing mathematical models and algorithms for computer networks. Then he worked at the Software Engineering Center of the Russian Academy of Sciences (RAS), at the Institute for System Analysis RAS and at the Institute for Information Transmission Problems RAS doing theoretical and applied research in the field of Mathematical modeling, Mathematical and Applied statistics, Intelligent Data Analysis, and their applications. At the same time, he had part-time full professor positions at the Moscow State Institute of Radiotechnics, Electronics and Automation, the National Research University Higher School of Economics and the Moscow Institute of Physics and technology.

His current research interests are in Mathematical modeling and Intelligent Data Analysis (including applied geometrical methods and Machine Learning) and their applications for the analysis of neuroimaging biomedical data. He has more than 150 scientific publications.

Current research Interests/Expertise: Mathematical modeling, Mathematical Statistics, Intelligent Data Analysis, Geometrical and Statistical methods in Data analysis, Manifold Learning, Machine Learning, Analysis of neuroimaging and biomedical data.

Leader of the Project “Machine Learning and Pattern Recognition for the development of diagnostic and clinical prognostic prediction tools in psychiatry, borderline mental disorders, and neurology” (2017-present) performed in the scope of Skoltech Biomedical Initiative.

Selected Publications (out of total 150, among them 30+ from 2014):

  • Nonlinear multi-output regression on unknown input manifold. Annals of Mathematics and Artificial Intelligence, vol. 81, №1-2, pp. 209-240, 2017 (with A. Kuleshov).
  • Manifold Learning in Data Mining Tasks. Lecture Notes in Artificial Intelligence Series, Vol. 8556, Springer, pp. 119-133, 2014 (with A.P. Kuleshov).
  • Low-Dimensional Data Representation in Data Analysis. Lecture Notes in Artificial Intelligence, vol. 8774, pp. 47-58, 2014 (with A.P. Kuleshov).
  • Manifold Learning in Regression tasks. Lecture Notes in Artificial Intelligence, Vol. 9407, Springer, pp. 414-423, 2015 (with A.P. Kuleshov, Y.A. Yanovich).
  • Extended Regression on Manifolds Estimation. Lecture Notes in Artificial Intelligence, vol. 9653 ‘Conformal and Probabilistic Prediction with Applications’, Heidelberg, Springer, pp. 208–228, 2016 (with A.P. Kuleshov).
  • Statistical Learning on Manifold-valued data. Lecture Notes in Artificial Intelligence, vol. 9729, Springer, pp. 311–325, 2016 (with A.P. Kuleshov).
  • Incremental Construction of Low-dimensional Data Representations. Lecture Notes in Artificial Intelligence, vol. 9896, pp. 55–67, Springer 2016 (with A.P. Kuleshov).
  • Mobile Robot Localization via Machine Learning. Lecture Notes in Artificial Intelligence, vol. 10358, Springer, pp. 276-290, 2017 (with A.P. Kuleshov, E.V. Burnaev).
  • Reinforcement Learning for Computer Vision and Robot Navigation. Lecture Notes in Computer Science, vol. 10935, Springer, pp. 258-272, 2018 (with E. Burnaev, O. Kachan).
  • Conformal Prediction in Manifold Learning. Proceedings of Machine Learning Research, vol. 91, pp. 234-253, 2018 (with A.P. Kuleshov, E.V. Burnaev).
  • Manifold learning regression with non-stationary kernels. Lecture Notes in Artificial Intelligence Series, vol. 11081, Springer, pp. 152–164, 2018 (with A.P. Kuleshov, E.V. Burnaev).
  • MRI-based Diagnostics of Depression Concomitant with Epilepsy: in Search of the Potential Biomarkers. Proceedings of the 5th International IEEE Conference on Data Science and Advanced Analytics (DSAA 2018), IEEE Computer Society, pp. 555-564, 2018 (with E. Burnaev, M. Sharaev, et al.).
  • Pattern Recognition Pipeline for Neuroimaging Data. Lecture Notes in Artificial Intelligence Series, vol. 11081, Springer, pp. 306-319, 2018 (with E. Burnaev, M. Sharaev, et al.).
  • Functional brain areas mapping in patients with glioma based on resting-state fMRI data decomposition. Proceedings of the IEEE International Conference on Data Mining 2018, Workshops Proceedings volume, IEEE Computer Society, pp. 292-298, 2018 (with M. Sharaev, E. Burnaev, et al.).
  • Learning Connectivity Patterns via Graph Kernels for fMRI-based Depression Diagnostics. Proceedings of the IEEE International Conference on Data Mining 2018, Workshops Proceedings volume, IEEE Computer Society, USA, pp. 308-314, 2018 (with M. Sharaev, E. Burnaev, et al.).
  • Manifold learning in statistical tasks. Uchenye Zapiski Kazanskogo Universiteta, Seriya Fiziko-Matematicheskie Nauki, v. 160, pp. 229-242, 2018.
  • MRI brain imagery processing software in data analysis. Transactions on mass-data analysis of images and signals, vol. 9, № 1, pp. 3-17, 2018 (with E. Kondrateva, S. Sushchinskaya, et al.).
  • Data analysis: predictive modelling and predictive maintenance. In: Advanced technologies for the aviation industry (analytical review), section 2.5, pp. 117-127, Publisher: Nauka, Moscow, 2017 (with E. Burnaev).

Lecturing on course “Geometrical Methods of Machine Learning”, supervizing of MS amd PhD students