ivanoseledets



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Ivan Oseledets

Ivan Oseledets graduated from Moscow Institute of Physics and Technology in 2006, got Candidate of Sciences
degree in 2007, and Doctor of Sciences in 2012, both from Marchuk Institute of Numerical Mathematics of
Russian Academy of Sciences. He joined Skoltech CDISE in 2013.
Ivan’s research covers a broad range of topics. He proposed a new decomposition of high-dimensional arrays
(tensors) – tensor-train decomposition, and developed many efficient algorithms for solving high-dimensional
problems. These algorithms are used in different areas of chemistry, biology, data analysis and machine
learning. His current research focuses on development of new algorithms in machine learning and artificial
intelligence such as construction of adversarial examples, theory of generative adversarial networks and
compression of neural networks. It resulted in publications in top computer science conferences such as ICML,
NIPS, ICLR, CVPR, RecSys, ACL and ICDM.
Professor Oseledets is an Associate Editor of SIAM Journal on Mathematics in Data Science, SIAM Journal on
Scientific Computing, Advances in Computational Mathematics (Springer). He is also an area chair of ICLR 2020
conference.
Ivan Oseledets got several awards for his research and industrial cooperation, including two gold medals of
Russian academy of Sciences (for students in 2005 and young researchers in 2009), Dynasty Foundation award
(2012), SIAM Outstanding Paper Prize (2018), Russian President Award for young researchers in science and
innovation (2018), Ilya Segalovich award for Best PhD thesis supervisor (2019), Best Professor award from
Skoltech (2019), the best cooperation project leader award from Huawei (2015, 2017). He also has been a Pi
and Co-Pi of several grants and industrial projects (230 million of rubles since 2017).
Professor Oseledets is actively involved in education and research supervision: he introduced and is teaching
three courses of Skoltech curriculum, and five of his PhD students have successfully defended their theses,
including two PhD students at Skoltech.

  • Solution of multidimensional integral and differential equations discretized on fine grids
  • Ab initio computations in quantum chemistry and computational material design
  • Construction of reduced order models for multiparametric systems in engineering
  • Uncertainty quantification in engineering sciences
  • Data mining and compression
[1] Roman Schutski, Danil Lykov, and Ivan Oseledets. Adaptive algorithm for quantum circuit simulation. Phys. Rev. A., 101(4):042335, 2020.
[2] Daria Fokina, Ekaterina Muravleva, George Ovchinnikov, and Ivan Oseledets. Microstructure synthesis using style-based generative adversarial networks. Phys. Rev. E., 101(4):043308, 2020.
[3] Anton Tsitsulin, Marina Munkhoeva, Davide Mottin, Panagiotis Karras, Alex Bronstein, Ivan Oseledets, and Emmanuel Müller. The shape of data: Intrinsic distance for data distributions. In ICLR 2020: Proceedings of the International Conference on Learning Representations, 2020.
[4] Valentin Khrulkov, Leyla Mirvakhabova, Evgeniya Ustinova, Ivan Oseledets, and Victor Lempitsky. Hyperbolic image embeddings. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
[5] G. Ovchinnikov, D. Zorin, and I. Oseledets. Robust regularization of topology optimization problems with a posteriori error estimators. Russian J. Numer. Anal. Math. Modell., 34(1):57–69, 2019.
[6] Valentin Khrulkov, Oleskii Hrinchuk, and Ivan Oseledets. Generalized tensor models for recurrent neural networks. In Proceedings of the International Conference on Learning Representations (ICLR), 2019.
[7] Anh-Huy Phan, Andrzej Cichocki, Ivan Oseledets, Giuseppe G Calvi, Salman Ahmadi-Asl, and Danilo P Mandic. Tensor networks for latent variable analysis: Higher order canonical polyadic decomposition. IEEE transactions on neural networks and learning systems, 2019.
[8] Maxim Rakhuba, Alexander Novikov, and Ivan Oseledets. Low-rank riemannian eigensolver for high-dimensional hamiltonians. Journal of Computational Physics, 396:718–737, 2019.
[9] Artyom Nikitin, Ilia Fastovets, Dmitrii Shadrin, Mariia Pukalchik, and Ivan Oseledets. Bayesian optimization for seed germination. Plant Methods, 15(1):43, 2019.
[10] Maria A Pukalchik, Alexandr M Katrutsa, Dmitry Shadrin, Vera A Terekhova, and Ivan V Oseledets. Machine learning methods for estimation the indicators of phosphogypsum influence in soil. Journal of Soils and Sediments, 19(5):2265–2276, 2019.
[11] Tsui-Wei Weng, Pin-Yu Chen, Lam M. Ngueyen, Mark S. Squillante, Ivan Oseledets, and Luca Daniel. PROVEN: certifying robustness of neural networks with probabilistic approach. In Proceedings of the 36th International Conference on Machine Learning, volume 81, pages 2621–2629, 2019.
[12] Maxim Rakhuba, Alexander Novikov, and Ivan Oseledets. Low-rank Riemannian eigensolver for high-dimensional Hamiltonians. J. Comput. Phys., 396(1):718–737, 2019.
[13] Ivan Sosnovik and Ivan Oseledets. Neural networks for topology optimization. Russ. J. Numer. Anal. Math. Modell., 34(4):215–223, 2019.
[14] Yury Kostyukevich, George Ovchinnikov, Alexey Kononikhin, Igor Popov, Ivan Oseledets, and Eugene Nikolaev. Thermal dissociation and H/D exchange of streptavidin tetramers at atmospheric pressure. International Journal of Mass Spectrometry, 427:100–106, 2018.
[15] A. Yu. Mikhalev and I. V. Oseledets. Rectangular maximum–volume submatrices and their applications. Linear Algebra Appl., 538:187–211, 2018.
[16] Valentin Khrulkov and Ivan Oseledets. Desingularization of bounded-rank matrix sets. SIAM J. Matrix Anal. Appl., 39(1):451–471, 2018.
[17] Valentin Khrulkov and Ivan Oseledets. Art of singular vectors and universal adversarial perturbations. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
[18] Valentin Khrulkov, Alexander Novikov, and Ivan Oseledets. Expressive power of recurrent neural networks. In Proceedings of the International Conference on Learning Representations (ICLR), 2018.
[19] Denis Kolesnikov and Ivan Oseledets. Convergence analysis of projected fixed-point iteration on a low-rank matrix manifold. Numer. Linear Algebra Appl, 25(5), 2018.
[20] Daria A. Sushnikova and Ivan V. Oseledets. “Compress and eliminate” solver for symmetric positive definite sparse matrices. SIAM J. Sci. Comput., 40(3):A1742–A1762, 2018.
[21] Maxim Rakhuba and Ivan Oseledets. Jacobi-Davidson method on low-rank matrix manifolds. SIAM J. Sci. Comput., 40(2):A1149–A1170, 2018.
[22] Vladislav Pimanov and Ivan Oseledets. Regularization of topology optimization problem by the FEM a posteriori error estimator. Struct. Multidiscp. O., 58(4), 2018.
[23] Valentin Khrulkov and Ivan Oseledets. Geometry Score: a method for comparing generative adversarial networks. In Proceedings of the 35th International Conference on Machine Learning, volume 80, pages 2621–2629, 2018.
[24] Ekaterina A. Muravleva and Ivan V. Oseledets. Approximate solution of linear systems with Laplace-like operators via cross approximation in the frequency domain. Comput. Meth. Appl. Math., 2018.
[25] Maxim Kuznetsov and Ivan V. Oseledets. Tensor train spectral method for learning of hidden Markov models (HMM). Comput. Meth. Appl. Math., 2018.
[26] E. Muravleva, I. Oseledets, and D. Koroteev. Application of machine learning to viscoplastic flow modeling. Phys. Fluids, 30(10):103102, 2018.
[27] Pavel Kharyuk, Dmitry Nazarenko, Ivan Oseledets, Igor Rodin, Oleg Shpigun, Andrey Tsitsilin, and Mikhail Lavrentyev. Employing fingerprinting of medicinal plants by means of LC-MS and machine learning for species identification task. Sci. Rep., 8(1):17053, 2018.
[28] Alexander Novikov, Mikhail Trofimov, and Ivan Oseledets. Tensor Train polynomial models via Riemannian optimization. Bull. Pol. Acad. of Sci., 66(6):789–797, 2018.
[29] Ivan Oseledets, Maxim Rakhuba, and André Uschmajew. Alternating least squares as moving subspace correction. SIAM J. Numer. Anal., 56(6):3459–3479, 2018.
[30] I. V. Oseledets, G. V. Ovchinnikov, and A. M. Katrutsa. Fast, memory efficient low-rank approximation of SimRank. Journal of Complex Networks, 5(1):111–126, 2017.
[31] Evgeny Frolov and Ivan Oseledets. Tensor methods and recommender systems. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(3), 2017.
[32] Grigory Drozdov, Igor Ostanin, and Ivan Oseledets. Time-and memory-efficient representation of complex mesoscale potentials. J. Comput. Phys., 343:110–114, 2017.
[33] Igor Ostanin, Denis Zorin, and Ivan Oseledets. Parallel optimization with boundary elements and kernel independent fast multipole method. International Journal of Computational Methods and Experimental Measurements, 5(2):154–162, 2017.
[34] Igor Ostanin, Denis Zorin, and Ivan Oseledets. Fast topological-shape optimization with boundary elements in two dimensions. Russian J. Numer. Anal. Math. Modell., 32(2):127–133, 2017.
[35] Andrzej Cichocki, Anh-Huy Phan, Qibin Zhao, Namgil Lee, Ivan Oseledets, Masashi Sugiyama, and Danilo Mandic. Tensor networks for dimensionality reduction and large-scale optimization: Part 2 applications and future perspectives. Foundations and Trends in Machine Learning, 9(6):431–673, 2017.
[36] Igor Ostanin, Ivan Tsybulin, Mikhail Litsarev, Ivan Oseledets, and Denis Zorin. Scalable topology optimization with the kernel-independent fast multipole method. Engineering Analysis with Boundary Elements, 83:123–132, 2017.
[37] Igor Ostanin, Alexander Safonov, and Ivan Oseledets. Natural erosion of sandstone as shape optimisation. Sci. Rep., 7(1):17301, 2017.
[38] A. Yu. Mikhalev and I. V. Oseledets. Iterative representing set selection fo nested cross approximation. Numer. Linear Algebra Appl., 23(2):230–248, 2016.
[39] M. S. Litsarev and I. V. Oseledets. Low-rank approach to the computation of path integrals. J. Comput. Phys., 305:557–574, 2016.
[40] M. V. Rakhuba and I. V. Oseledets. Grid-based electronic structure calculations: the tensor decomposition approach. J. Comput. Phys., 2016.
[41] Vladimir Kazeev, Ivan Oseledets, Maxim Rakhuba, and Christoph Schwab. QTT-finite-element approximation for multiscale problems I: model problems in one dimension. Adv. Comput. Math., 2016.
[42] Daria A. Sushnikova and Ivan V. Oseledets. Preconditioners for hierarchical matrices based on their extended sparse form. Russ. J. Numer. Anal. Math. Modelling, 31(1):29–40, 2016.
[43] Maxim Rakhuba and Ivan Oseledets. Calculating vibrational spectra of molecules using tensor train decomposition. J. Chem. Phys., 145:124101, 2016.
[44] Evgeny Frolov and Ivan Oseledets. Fifty shades of ratings: How to benefit from a negative feedback in top-n recommendations tasks. In Proceedings of the 10th ACM Conference on Recommender Systems, RecSys ’16, pages 91–98, 2016.
[45] D. V. Nazarenko, P. V. Kharyuk, I. V. Oseledets, I. A. Rodin, and O. A. Shpigun. Machine learning for LC-MS medicinal plants identification. Chemometrics and Intelligent Laboratory Systems, 156:174–180, 2016.
[46] Alexander Fonarev, Alexander Mikhalev, Pavel Serdyukov, Gleb Gusev, and Ivan Oseledets. Efficient rectangular maximal-volume algorithm for rating elicitation in collaborative filtering. In Data Mining (ICDM), 2016 IEEE 16th International Conference on, pages 141–150, 2016.
[47] Ivan V. Oseledets, Maxim V. Rakhuba, and Andrei V. Chertkov. Black-box solver for multiscale modelling using the QTT format. In Proc. ECCOMAS, Crete Island, Greece, 2016.
[48] Jutho Haegeman, Christian Lubich, Ivan Oseledets, Bart Vandereycken, and Frank Verstraete. Unifying time evolution and optimization with matrix product states. Phys. Rev. B, 94(16):165116, 2016.
[49] Andrzej Cichocki, Namgil Lee, Ivan Oseledets, Anh-Huy Phan, Qibin Zhao, Danilo P Mandic, and others. Tensor networks for dimensionality reduction and large-scale optimization: Part 1 low-rank tensor decompositions. Foundations and Trends in Machine Learning, 9(4-5):249–429, 2016.
[50] Zhang Zheng, Xiu Yang, Ivan V. Oseledets, George Em Karniadakis, and Luca Daniel. Enabling high–dimensional hierarchical uncertainty quantification by ANOVA and Tensor–Train decomposition. IEEE Trans. Comput-aided Des. Integr. Circuits Syst., 34(1):63–76, 2015.
[51] M. V. Rakhuba and I. V. Oseledets. Fast multidimensional convolution in low-rank tensor formats via cross approximation. SIAM J. Sci. Comput., 37(2):A565–A582, 2015.
[52] Ch. Lubich, I. Oseledets, and B. Vandereycken. Time integration of tensor trains. SIAM J. Numer. Anal., 53(2):917–941, 2015.
[53] M. S. Litsarev and I. V. Oseledets. Fast low-rank approximations of multidimensional integrals in ion-atomic collisions modelling. Numer. Linear Algebra Appl., 22(6):1147–1160, 2015.
[54] G.V. Ryzhakov, A.Yu. Mikhalev, D.A. Sushnikova, and I.V. Oseledets. Numerical solution of diffraction problems using large matrix compression. In Antennas and Propagation (EuCAP), 2015 9th European Conference on, pages 1–3, April 2015.
[55] D. A. Kolesnikov and I. V. Oseledets. From low-rank approximation to arational Krylov subspace method for the Lyapunov equation. SIAM J. Matrix Anal. Appl., 36(4):1622–1637, 2015.
[56] V. Baranov and I. Oseledets. Fitting high-dimensional potential energy surface using active subspace and tensor train (AS+TT) method. J. Chem. Phys., (143):17107, 2015.
[57] Sergey I. Kabanikhin, Nikita S. Novikov, Ivan V. Oseledets, and Maxim A. Shishlenin. Fast Toeplitz linear system inversion for solving two-dimensional acoustic inverse problem. Inverse Problems, 23(6):687–700, 2015.
[58] I. Ostanin, A. Mikhalev, D. Zorin, and I. Oseledets. Engineering optimization with the fast boundary element method. WIT Transactions on Modelling and Simulation, 61:7, 2015.
[59] Christian Lubich and Ivan V. Oseledets. A projector-splitting integrator for dynamical low-rank approximation. BIT, 54(1):171–188, 2014.
[60] Ivan Oseledets. Solving high-dimensional problems via stable and efficient tensor factorization techniques. In Abstracts of Papers of the American Chemical Society, volume 246, pages 244–Phys, 2014.
[61] S. V. Dolgov, B. N. Khoromskij, I. V. Oseledets, and D. V. Savostyanov. Computation of extreme eigenvalues in higher dimensions using block tensor train format. Computer Phys. Comm., 185(4):1207–1216, 2014.
[62] Anwesha Chaudhury, Ivan Oseledets, and Rohit Ramachandran. A computationally efficient technique for the solution of multi-dimensional PBMs of granulation. Comput. Chem. Eng., 61(11):234–244, 2014.
[63] M. A. Botchev, I. V. Oseledets, and E. E. Tyrtyshnikov. Iterative across-time solution of linear differential equations: Krylov subspace versus waveform relaxation. Comput. Math. Appl., 67(2):2088–2098, 2014.
[64] Mikhail S. Litsarev and Ivan V. Oseledets. The DEPOSIT computer code based on the low rank approximations. Computer Phys. Comm., 185(10):2801–2082, 2014.
[65] P. A. Absil and I. V. Oseledets. Low-rank retractions: a survey and new results. Comput. Optim. Appl., 2014.
[66] I. V. Oseledets. Constructive representation of functions in low-rank tensor formats. Constr. Approx., 37(1):1–18, 2013.
[67] Vladimir Lyashev, Ivan Oseledets, and Delai Zheng. Tensor-based multiuser detection and intra-cell interference mitigation in LTE PUCCH. In Proc. TELFOR 2013, pages 385–388, 2013.
[68] Sergey Dolgov, Boris N. Khoromskij, Ivan V. Oseledets, and Eugene E. Tyrtyshnikov. A reciprocal preconditioner for structured matrices arising from elliptic problems with jumping coefficients. Linear Algebra Appl., 436(9):2980–3007, 2012.
[69] I. V. Oseledets and A. Yu Mikhalev. Representation of quasiseparable matrices using excluded sums and equivalent charges. Linear Algebra Appl., 436(3):699–708, 2012.
[70] S. V. Dolgov, Boris N. Khoromskij, Ivan V. Oseledets, and Eugene E. Tyrtyshnikov. Low-rank tensor structure of solutions to elliptic problems with jumping coefficients. J. Comput. Math., 30(1):14–23, 2012.
[71] S. A. Goreinov, I. V. Oseledets, and D. V. Savostyanov. Wedderburn rank reduction and Krylov subspace method for tensor approximation. Part 1: Tucker case. SIAM J. Sci. Comput., 34(1):A1–A27, 2012.
[72] I. V. Oseledets and S. V. Dolgov. Solution of linear systems and matrix inversion in the TT-format. SIAM J. Sci. Comput., 34(5):A2718–A2739, 2012.
[73] S. V. Dolgov, B. N. Khoromskij, and I. V. Oseledets. Fast solution of multi-dimensional parabolic problems in the tensor train/quantized tensor train–format with initial application to the Fokker-Planck equation. SIAM J. Sci. Comput., 34(6):A3016–A3038, 2012.
[74] B. N. Khoromskij and I. V. Oseledets. QTT-approximation of elliptic solution operators in higher dimensions. Rus. J. Numer. Anal. Math. Model., 26(3):303–322, 2011.
[75] I. V. Oseledets. Tensor-train decomposition. SIAM J. Sci. Comput., 33(5):2295–2317, 2011.
[76] I. V. Oseledets and E. E. Tyrtyshnikov. Algebraic wavelet transform via quantics tensor train decomposition. SIAM J. Sci. Comput., 33(3):1315–1328, 2011.
[77] I. V. Oseledets. DMRG approach to fast linear algebra in the TT–format. Comput. Meth. Appl. Math., 11(3):382–393, 2011.
[78] I. V. Oseledets, E. E. Tyrtyshnikov, and N. L. Zamarashkin. Tensor-train ranks of matrices and their inverses. Comput. Meth. Appl. Math, 11(3):394–403, 2011.
[79] I. V. Oseledets. Improved n-term Karatsuba-like formulas in GF(2). IEEE Trans. Computers, 60(8):1212–1216, 2011.
[80] D. V. Savostyanov and I. V. Oseledets. Fast adaptive interpolation of multi-dimensional arrays in tensor train format. In Proceedings of 7th International Workshop on Multidimensional Systems (nDS). IEEE, 2011.
[81] I. V. Oseledets. Tensor train decomposition for low-parametric representation of high-dimensional arrays and functions: Review of recent results. In Proceedings of 7th International Workshop on Multidimensional Systems (nDS). IEEE, 2011.
[82] I. V. Oseledets, D. V. Savostyanov, and E. E. Tyrtyshnikov. Cross approximation in tensor electron density computations. Numer. Linear Algebra Appl., 17(6):935–952, 2010.
[83] I. V. Oseledets and E. A. Muravleva. Fast orthogonalization to the kernel of discrete gradient operator with application to the Stokes problem. Linear Algebra Appl., 432(6):1492–1500, 2010.
[84] I. V. Oseledets and E. E. Tyrtyshnikov. TT-cross approximation for multidimensional arrays. Linear Algebra Appl., 432(1):70–88, 2010.
[85] B. N. Khoromskij and I. V. Oseledets. Quantics-TT collocation approximation of parameter-dependent and stochastic elliptic PDEs. Comput. Methods Appl. Math., 10(4):376–394, 2010.
[86] I. V. Oseledets. Approximation of 2d ×2d matrices using tensor decomposition. SIAM J. Matrix Anal. Appl., 31(4):2130–2145, 2010.
[87] I. V. Oseledets, E. E. Tyrtyshnikov, and N. L. Zamarashkin. Matrix inversion cases with size-independent rank estimates. Linear Algebra Appl., 431(5-7):558–570, 2009.
[88] I. V. Oseledets, S. L. Stavtsev, and E. E. Tyrtyshnikov. Integration of oscillating functions in a quasi-threedimensional electrodynamic problem. Comput. Math. Math. Phys, 49(2):301–312, 2009.
[89] I. V. Oseledets, D. V. Savostyanov, and E. E. Tyrtyshnikov. Fast simultaneous orthogonal reduction to triangular matrices. SIAM J. Matrix Anal. Appl., 31(2):316–330, 2009.
[90] I. V. Oseledets, D. V. Savostyanov, and E. E. Tyrtyshnikov. Linear algebra for tensor problems. Computing, 85(3):169–188, 2009.
[91] I. V. Oseledets and E. E. Tyrtyshnikov. Breaking the curse of dimensionality, or how to use SVD in many dimensions. SIAM J. Sci. Comput., 31(5):3744–3759, 2009.
[92] I. V. Oseledets. Approximation of matrices with logarithmic number of parameters. Doklady Math., 428(1):23–24, 2009.
[93] I. V. Oseledets and E. E. Tyrtyshnikov. Recursive decomposition of multidimensional tensors. Doklady Math., 427(1):14–16, 2009.
[94] I. V. Oseledets, E. E. Tyrtyshnikov, and N. L. Zamarashkin. Tensor structure of the inverse of a banded Toeplitz matrix. Doklady Math., 80(2):669–670, 2009.
[95] I. V. Oseledets. A new tensor decomposition. Doklady Math., 80(1):495–496, 2009.
[96] I. V. Oseledets. Optimal Karatsuba-like formulae for certain bilinear forms in GF(2). Linear Algebra Appl., 429(8):2052–2066, 2008.
[97] V. Olshevsky, I. V. Oseledets, and E. E. Tyrtyshnikov. Superfast inversion of two-level Toeplitz matrices using Newton iteration and tensor-displacement structure. Operator Theory: Advances and Applications, 179:229–240, 2008.
[98] I. V. Oseledets, D. V. Savostianov, and E. E. Tyrtyshnikov. Tucker dimensionality reduction of three-dimensional arrays in linear time. SIAM J. Matrix Anal. Appl., 30(3):939–956, 2008.
[99] I. V. Oseledets. The integral operator with logarithmic kernel has only one positive eigenvalue. Linear Algebra Appl., 428(7):1560–1564, 2008.
[100] I. V. Oseledets. Lower bounds for separable approximations of the Hilbert kernel. Mat. Sb., 198(3):137–144, 2007.
[101] V. Olshevsky, I. V. Oseledets, and E. E. Tyrtyshnikov. Tensor properties of multilevel Toeplitz and related matrices. Linear Algebra Appl., 412(1):1–21, 2006.
[102] I. V. Oseledets and D. V. Savostyanov. Minimization methods for approximating tensors and their comparison. Comput. Math. Math. Phys., 46(10):1641–1650, 2006.
[103] I. V. Oseledets and E. E. Tyrtyshnikov. A unifying approach to the construction of circulant preconditioners. Linear Algebra Appl., 418(2-3):435–449, 2006.
[104] I. V. Oseledets, E. E Tyrtyshnikov, and N. L. Zamarashkin. On the approximation of Toeplitz matrices by a sum of the circulant and low-rank matrix. Doklady Math., 406(5):602–603, 2006.
[105] I. V. Oseledets and E. E. Tyrtyshnikov. Approximate inversion of matrices in the process of solving a hypersingular integral equation. Comput. Math. Math. Phys., 45(2):302–313, 2005.
[106] I. V. Oseledets. Use of divided differences and B-splines for constructing fast discrete transforms of wavelet type on nonuniform grids. Math. Notes, 77(5-6):686–694, 2005.
[107] J. M. Ford, I. V. Oseledets, and E. E. Tyrtyshnikov. Matrix approximations and solvers using tensor products and non-standard wavelet transforms related to irregular grids. Russ. J. Numer. Anal. Math. Modelling, 19(2):185–204, 2004.
  • Russian President award for Young Scientists in Science and Innovation (2018)
  • SIAM Outstanding paper prize (2018)
  • Ilya Segalovich award for best computer science advisor (2019)
  • Best cooperation project leader award (Huawei, 2015, 2017)
  • Gold medal from the Russian Academy of Sciences for best student work in Mathematics (2005)
  • Gold medal from the Russian Academy of Sciences for the best work among young scientists in Mathematics, joint with D. V. Savostyanov (2010)
raghavendrabelurjana
Raghavendra Belur Jana
Senior Research Scientist
georgeovchinnikov
George Ovchinnikov
Senior Research Scientist
Alexey Boyko
Alexey Boyko
PhD student
andreychertkov
Andrey Chertkov
PhD student
evgeniifrolov
Evgenii Frolov
PhD student
Alexey Golovizin
Research Engineer
juliagusak
Julia (Yulia) Gusak
Research Scientist
sergeymatveev
Sergey Matveev
Research Scientist
leylamirvakhabova
Leyla Mirvakhabova
PhD student
igorostanin
Igor Ostanin
Research Scientist
annapetrovskaia
Anna Petrovskaia
PhD student
glebryzhakov
Gleb Ryzhakov
Research Scientist
talgatdaulbaev
Talgat Daulbaev
PhD student
daniilmerkulov
Daniil Merkulov
PhD student
evgenyponomarev
Evgeny Ponomarev
PhD student
  • Numerical Linear Algebra, an obligatory course for 1st year Master students in Data Science and Computational Data
  • Fast methods for partial differential and integral equations, elective course for 2nd year Master students

ФИО: Оселедец Иван Валерьевич

Занимаемая должность (должности): Профессор

Преподаваемые дисциплины: Вычислительная линейная алгебра

Ученая степень: Доктор физико-математических наук, 2012 Институт вычислительной математики РАН, Москва; Кандидат физико-математических наук, 2007, Институт вычислительной математики РАН, Москва

Ученое звание (при наличии): нет

Наименование направления подготовки и/или специальности: Прикладная математика и физика

Данные о повышении квалификации и/или профессиональной переподготовке (при наличии): нет

Общий стаж работы: 14 лет

Стаж работы по специальности: 14 лет