Vadim Strijov is a professor at the Moscow Institute of Physics and Technology, head of the Department of Intelligent Systems MIPT. He is graduated from the Ufa State Aviation Technical University in 1992. In 2002 he obtained PhD from the Computer Center of the Russian Academy of Sciences with his thesis on Expert estimations concordance. In 2014 he obtained a degree of Doctor of Sciences in Physics and Mathematics with his thesis on probabilistic model generation and multi-model selection. He is an author of more than two hundred scientific papers on machine learning and data analysis. Eight PhD theses were defended with his supervision. In 2020 he was awarded the Ilya Segalovich Prize for his significant contribution to the development of the scientific community.
Professional interests: multivariate statistics, machine learning, functional data analysis, model generation and selection, Bayesian multimodeling, spatial time series analysis, biomedical signal analysis.
- Grabovoy A.V., Strijov V.V. Bayesian distilling of deep learning models // Automation and Remote Control, 2021, 10(82): 1846–1856. DOI
- Bakhteev O.Y., Strijov V.V. Comprehensive analysis of gradient-based hyperparameter optimization algorithmss // Annals of Operations Research, 2020: 1-15. DOI
- Isachenko R.V., Strijov V.V. Quadratic Programming Optimization with Feature Selection for Non-linear Models // Lobachevskii Journal of Mathematics, 2018, 39(9): 1179-1187. DOI
- Motrenko A.P., Strijov V.V. Multi-way feature selection for ECoG-based brain-computer interface // Expert Systems with Applications, 2018, 114(30): 402-413. DOI
- Kulunchakov A.S., Strijov V.V. Generation of simple structured Information Retrieval functions by genetic algorithm without stagnation // Expert Systems with Applications, 2017, 85: 221-230. DOI
- Aduenko A.A., Motrenko A.P., Strijov V.V. Object selection in credit scoring using covariance matrix of parameters estimations // Annals of Operations Research, 2018, 260(1-2): 3-21. DOI
- Katrutsa A.M., Strijov V.V. Comprehensive study of feature selection methods to solve multicollinearity problem according to evaluation criteria // Expert Systems with Applications, 2017, 76: 1-11. DOI