Maths as a Second Language

Aleksandr Katrutsa

Research scientist, Skoltech Senior research scientist, Artificial Intelligence Research Institute

Faculty Profile

Aleksandr is a research scientist at Skoltech and a senior research scientist at the Artificial Intelligence Research Institute. He defended his PhD thesis in 2019 at the Moscow Institute of Physics and Technology (MIPT). The focus of his research is on the intersection of machine learning methods and numerical analysis. Aleksandr is the author of multiple papers published in top-tier journals and conferences. Also, he participated in industrial projects that were successfully deployed in production. Aleksandr created and delivered courses on numerical linear algebra and optimisation methods at Yandex School of Data Analysis, AI Masters, and MIPT.

Experience

2021 - Present
Senior researcher
Artificial Intelligence Research Institute, Moscow, Russia.
2020 - Present
Research scientist
CAIT, Skolkovo Institute of Science and Technology.
2018 - 2020
Research intern
CDISE, Skolkovo Institute of Science and Technology
09.2015 – 12.2015
Intern
INRIA, Grenoble, France, NANO-D team

Education

2016 - 2019
PHD Computational mathematics
Moscow Institute of Physics and Technology
2014 - 2016
MS Information systems
Skolkovo Institute of Science and Technology
2010 - 2014
BS data science
Moscow Institute of Physics and Technology

Publications

2024
Multiparticle Kalman filter for object localization in symmetric environments. Expert Systems with Applications. Vol 237, 121408
R. Korkin, I. Oseledets, A. Katrutsa.
2023
Extension of dynamic mode decomposition for dynamic systems with in- complete information based on t-model of optimal prediction. Journal of Computational Physics. Vol. 476, 111913
A. Katrutsa, S. Utyuzhnikov, I. Oseledets.
2020
Black-box learning of multigrid parameters. Journal of Computational and Applied Mathematics. Vol. 368, 112524
A. Katrutsa, T. Daulbaev, and I. Oseledets.
2020
Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs. Advances in Neural Information Processing Systems, 2020, 33., A*
T. Daulbaev, A. Katrutsa, L. Markeeva, J. Gusak, A. Cichocki, I. Oseledets.
2019
Machine learning methods for es- timation the indicators of phosphogypsum influence in soil. Journal of Soils and Sediments, 19, 2265-2276
Pukalchik, M. A., Katrutsa, A. M., Shadrin, D., Terekhova, V. A., Oseledets, I. V.
2017
Comprehensive study offeature selectionmethods to solvemulticollinearity problem according to evaluation criteria, Expert Systems with Applications, Vol. 76, 1-11
A. Katrutsa, V. Strijov.
2017
2017. Fast, memory-efficient low-rank approximation of SimRank. Jour- nal of Complex Networks, 5(1), 111-126
Oseledets, I. V., Ovchinnikov, G. V., Katrutsa, A. M.
2015
Stress test procedure for feature selection algorithms. Chemometrics and Intelligent Laboratory Systems, Vol. 142 : 172-183
Katrutsa A. M., Strijov V. V.

Courses by Aleksandr

Maths as a Second Language

Numerical Linear Algebra & Optimization

02 Dec - 20 Dec 2024

Barcelona

the university of the future

We believe the future is bright if you make it so.

Explore Harbour.Space

Faq