Vadim

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Vadim is a Doctor of Sciences in Physics and Mathematics, a professor and principal investigator at the Computing Center RAS.

Vadim

Fields of research

  • Machine Learning and Data Analysis, Deep Learning, Bayesian model selection
  • Functional and Geometric Learning, Physics-Informed learning
  • Neuroscience, Behavioural Analysis, and Brain-Computer Wearable Interfaces

Research Statement

Roles

Makes long-­term planning and accomplishes applied and theoretical research. Runs and leads research projects in AI: states problems, connects researchers and programmers, and delivers projects to their implementation and publications.

Duties

  • 2004–2022 Doctor of physico-mathematical sciences, Principal investigator at the Computing Center of the Russian Academy of Sciences
  • 2010–2022 Professor, chief of department at the Moscow Institute of Physics and Technology
  • 2019–2022 Chief scientist of the Laboratory of Machine Intelligence at MIPT
  • 2006 Consultant on human behaviour analysis projects at Forecsys Ltd
  • 2015-2022 Chair of the BS, MS, and PhD thesis committees at the University Higher School of Economics and Skoltech
  • 2011–2019 Editor in chief of the Journal of Machine Learning and Data Analysis

Teaching courses

  • My First Scientific Paper: each year this course delivers over 30 supervised student projects and publications
  • Mathematical Forecasting – Signal and Functional Data Analysis
  • Machine learning and Data analysis: in 2015 with colleagues obtained a grant from Coursera.org to create a MOOC specialization, eight consequent courses
  • 2018–2022 Plans and runs five courses with his PhD alumni:

Supervision of research activities

PhD thesis, supervised and defended. Now the students are working for Amazon, Yahoo, Meta, WorldQuant:

  1. 2014. Algebraic graph transformations for non-linear model generation (Roman Sologub)
  2. 2015. Concordance of the partial ordered expert estimations (Mikhail Kuznetsov)
  3. 2017. Multi-model selection for classification problem (Arsenty Kuzmin)
  4. 2017. Hierarchical topic modelling for short-text collections (Alexander Aduenko)
  5. 2019. Multi-way feature selection for ECoG-based Brain-Computer Interface (Anastasiya Motrenko)
  6. 2020. Bayesian model selection for deep learning neural network structures (Oleg Bakhteev)
  7. 2021. Dimensionality reduction for ECoG Brain-Computer Interface time series (Roman Isachenko)
  8. 2022. Expert learning and Bayesian multi-modelling (Andrey Grabovoy)
  9. 2023. Spatial-time series multiple alignment and clustering, scheduled (Alexey Goncharov)
  10. 2024. Continuous space-time differential models for Brain-Computer Interface, planned (Alina Samokhina)

Organisational duties

  • Responsible for French Institute for Research in Computer Science and Automation INRIA-MIPT collaboration
  • Examiner of the PhD thesis committee at the Grenoble-Alpes University
  • Member of PhD thesis committee at the Russian Academy of Sciences, Computing Centre
  • Chair of the MS/PhD thesis committee at the Skolkovo Institute of Science and Technology
  • Chair of the BS thesis committees at the National Research University Higher School of Economics
  • Editor in chief of the Journal of Machine Learning and Data Analysis
  • Executive chair of the International Conference on Intelligent Data Processing
  • Program committee member of the International Federation of Operational Research Societies

Visiting professor

  • 2023 Worcester Technical Institute (USA): machine learning models for Brain signals: generative state space and Riemannian geometry
  • 2019 National Institute of Automation and Informatics (France): plans and organizes scientific research projects in machine learning for bioinformatics
  • 2015 University of Grenoble, Computer Science Laboratory (France): participates in research projects, devoted to industrial time series forecasting
  • 2014 RWTH Aachen University (Germany): delivers a course on Preference Learning and Model Selection
  • 2014 Middle East Technical University (Turkey): delivers a course on Model Selection in Machine learning
  • 2013 University of Siegen (Germany): delivers a course on Data Analysis in Business Analytics

Education

Obtained D.Sc.(2014) and Ph.D.(2002) in Physics and Mathematics with theses on Mathematical Modelling and Machine Learning from Russian Academy of Sciences, Computing Center

Distinctions and supervised grants

Yandex Segalovich prize award in 2020 for his significant impact in the scientific community development in the CIS-countries. Series of long-term grants from the Foundation for Basic Research:

  • 2004. Recognition and forecasting of emergency situations in difficult situations on multidimensional temporary restrictions
  • 2007. Development of the theory of search for regression models in an implicitly set set
  • 2010. Development of the theory of inductive generation and choice of models
  • 2011. Modeling and prognosis of prices for financial markets Intellectual data analysis; Creation of new tools for early warning and management of crisis risks
  • 2012. Methods of generating prognostic models of operational (online) diagnosis of rolling stock
  • 2013. Development of the theory of choice of hierarchical models in solving the problems of structural learning
  • 2016. Development of the theory of constructing superposition of universal models of signal classification
  • 2017. Algorithms of hierarchical forecasting demand for railway cargo transportation
  • 2019. Development of the theory of generating models of local approximation for classifying signals of wearable devices

Impact projects

  • The physical activity behaviour analysis: resulted in a set of algorithms for deployment in the wearable devices. Proceeded as three joint start-­up companies, which are successfully developing.
  • Multi­way feature selection for ECoG­-based Brain-­Computer Interface resulted as a feature selection algorithms and a forecasting model to decode the human upper limb movement using the electrocorticogram
  • Railroad time series forecasting for the county railroads resulted a set of the hierarchical time series forecasting algorithms to deploy to the freight planning
  • Creation the system of decision making for The Foundation for Basic Research
  • The theory of model generation and selection. The project resulted as a joint MS program of Laboratory of Machine Intelligence MIPT and University Grenoble­-Alpes
  • The theory of expert assessment concordance for decision making. The project was made for WWF and IUCN to rank protected areas, national parks and wilderness areas

Created dissemination media

Patents

  • Particle Detector. European Patent Office, patent 06808733.7-1240 PCT/GB2006060369
  • Time series generation for railroad freight models. Software program register, patent 2016617271
  • Model of railroad freight volumes forecasting. Software program register, patent 2016617272

Skills

Scientific and applied research planning and executing, Experiment planning, Electronic devices developing: programmable microchips and printed circuit boards, Programming VHDL, Matlab, Mathematica, C++, Python, Editorial duties and publishing

Selected Papers

  1. Quadratic programming feature selection for multicorrelated signal decoding with partial least squares (2022) Expert Systems with Applications by Isachenko R.V., Strijov Vadim DOI
  2. Numerical methods of sufficient sample size estimation for generalised linear models (2022) Lobachevskii Journal of Mathematics by Grabovoy A.V., Gadaev T.S., Motrenko A.P., Strijov Vadim
  3. Continuous physical activity recognition for intelligent labour monitoring (2022) Multimedia Tools and Applications by Motrenko A.P., Simchuk E., Khairullin R., Inyakin A., Kashirin D., Strijov Vadim DOI
  4. Bayesian Distillation of Deep Learning Models (2021) by Grabovoy A. V., Strijov Vadim DOI
  5. Prior distribution selection for a mixture of experts (2021) Computational Mathematics and Mathematical Physics by Grabovoy A.V., Strijov Vadim DOI
  6. Disconnected graph neural network for atom mapping in chemical reactions ( 2020) Physical Chemistry Chemical Physics by Nikitin F., Isayev O., Strijov Vadim DOI
  7. Quasi-periodic time series clustering for human activity recognition (2020) Lobachevskii Journal of Mathematics by Grabovoy A.V., Strijov Vadim DOI
  8. Hierarchical thematic classification of major conference proceedings (2020) CICLing by Kuzmin A.A., Aduenko A.A., Strijov Vadim URL
  9. Comprehensive analysis of gradient-based hyperparameter optimisation algorithms (2019) Annals of Operations Research by Bakhteev O.Y., Strijov Vadim DOI
  10. Object selection in credit scoring using covariance matrix of parameters estimations (2018) Annals of Operations Research by Aduenko A.A., Motrenko A.P., Strijov Vadim DOI
  11. Deep learning model selection of suboptimal complexity (2018) Automation and Remote Control by Bakhteev O.Y., Strijov Vadim DOI
  12. Quadratic programming optimisation with feature selection for non-linear models (2018) Lobachevskii Journal of Mathematics by Isachenko R.V., Strijov Vadim DOI
  13. Multi-way feature selection for ECoG-based brain-computer interface (2018) Expert Systems with Applications by Motrenko A.P., Strijov Vadim DOI
  14. Time series forecasting using RNNs: an extended attention mechanism to model periods and handle missing values (2017) ICONIP 2017 by Cinar Y.G., Mirisaee H., Goswami P., Gaussier E., Ait-Bachir A., Strijov Vadim URL
  15. Comprehensive study of feature selection methods to solve multicollinearity problem according to evaluation criteria (2017) Expert Systems with Applications by Katrutsa A.M., Strijov Vadim DOI
  16. Generation of simple structured Information Retrieval functions by genetic algorithm without stagnation (2017) Expert Systems with Applications by Kulunchakov A.S., Strijov Vadim
  17. Selecting an optimal model for forecasting the volumes of railway goods transportation (2017) Automation and Remote Control by Rudakov K.V., Kuznetsov M.P., Motrenko A.P., Stenina M.M., Kashirin D.O., Strijov Vadim DOI
  18. Extracting fundamental periods to segment human motion time series (2016) IEEE Journal of Biomedical and Health Informatics by Motrenko A.P., Strijov Vadim DOI
  19. Analytic and stochastic methods of structure parameter estimation (2016) Informatica by Kuznetsov M.P., Tokmakova A.A., Strijov Vadim DOI
  20. Stress-test procedure for feature selection algorithms (2015) Chemometrics and Intelligent Laboratory Systems by Katrutsa A.M., Strijov VadimDOI
  21. Ordinal classification using Pareto fronts (2015) Expert Systems with Applications by Stenina M.M., Kuznetsov M.P., Strijov Vadim DOI
  22. Supervised topic classification for modeling a hierarchical conference structure (2015) in S. Arik et al. (Eds.): International conference on neural information processing, Part 1, LNCS NIPS by Kuznetsov M.P., Clausel M., Amini M.-R., Gaussier E., Strijov Vadim DOI
  23. Editorial of the special issue data analysis and intelligent optimization with applications (2015) Machine Learning 101(1-3): 1-4 by Strijov Vadim, Weber G.W., Weber R., Sureyya O.A. DOI
  24. Methods of expert estimations concordance for integral quality estimation (2014) Expert Systems with Applications by Kuznetsov M.P., Strijov Vadim DOI
  25. Bayesian sample size estimation for logistic regression (2014) Journal of Computational and Applied Mathematics by Motrenko A.P., Strijov Vadim, Weber G.W. DOI
  26. Evidence optimisation for consequently generated models (2013) Mathematical and Computer Modelling by Strijov Vadim, Krymova E.A., Weber G.W. DOI
  27. Integral indicator of ecological impact of the Croatian thermal power plants (2011) Energy by Strijov Vadim, Granic G., Juric J., Jelavic B., Maricic S.A. DOI
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