Personal Websites

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,
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
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. A. V. Chertkov, I. V Oseledets, and M. V. Rakhuba. Robust discretization in quantized tensor train format for elliptic problems in two dimensions. arXiv preprint 1612.01166, 2016. URL: http://arxiv.org/abs/1612.01166. [ bib ]
    2. A. Cichocki, N. Lee, I. V. Oseledets, A. H. Phan, Q. Zhao, and D. Mandic. Low-rank tensor networks for dimensionality reduction and large-scale optimization problems: perspectives and challenges part 1. arXiv preprint 1609.00893, 2016. accepted at Trends and Foundations in Machine Learning. URL: http://arxiv.org/abs/1609.00893. [ bib ]
    3. Alexander Fonarev, Alexander Mikhalev, Pavel Serdyukov, Gleb Gusev, and Ivan Oseledets. Efficient rectangular maximal-volume algorithm for rating elicitation in collaborative filtering. arXiv preprint 1610.04850, 2016. accepted at ICDM 2016. URL: http://arxiv.org/abs/1610.04850. [ bib ]
    4. 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, 91–98. 2016. URL: http://arxiv.org/abs/1607.04228, doi:10.1145/2959100.2959170. [ bib ]
    5. Evgeny Frolov and Ivan Oseledets. Tensor methods and recommender systems. arXiv preprint 1603.06038, 2016. URL: http://arxiv.org/abs/1603.06038. [ bib ]
    6. 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. URL: http://arxiv.org/abs/1408.5056, doi:10.1103/PhysRevB.94.165116. [ bib ]
    7. Vladimir Kazeev, Ivan Oseledets, Maxim Rakhuba, and Christoph Schwab. QTT-finite-element approximation for multiscale problems I: model problems in one dimension. Adv. Comp. Math., 2016. URL: http://www.sam.math.ethz.ch/reports/2016/06, doi:10.1007/s10444-016-9491-y. [ bib ]
    8. Valentin Khrulkov and Ivan Oseledets. Desingularization of bounded-rank matrix sets. arXiv preprint 1612.03973, 2016. URL: http://arxiv.org/abs/1612.03973. [ bib ]
    9. Denis Kolesnikov and Ivan Oseledets. Convergence analysis of projected fixed-point iteration on a low-rank matrix manifold. arXiv preprint 1604.02111, 2016. URL: http://arxiv.org/abs/1604.02111. [ bib ]
    10. M. S. Litsarev and I. V. Oseledets. Low-rank approach to the computation of path integrals. J. Comp. Phys., 305:557–574, 2016. URL: http://arxiv.org/abs/1504.06149, doi:10.1016/j.jcp.2015.11.009. [ bib ]
    11. A. Yu. Mikhalev and I. V. Oseledets. Iterative representing set selection fo nested cross approximation. Numer. Linear Algebra Appl., 23(2):230–248, 2016. URL: http://arxiv.org/abs/1309.1773, doi:10.1002/nla.2021. [ bib ]
    12. 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. URL: http://www.sciencedirect.com/science/article/pii/S0169743916301368, doi:10.1016/j.chemolab.2016.06.003. [ bib ]
    13. Alexander Novikov, Mikhail Trofimov, and Ivan Oseledets. Tensor Train polynomial models via Riemannian optimization. arXiv preprint 1605.03795, 2016. URL: http://arxiv.org/abs/1605.03795. [ bib ]
    14. I. V. Oseledets, G. V. Ovchinnikov, and A. M. Katrutsa. Fast, memory efficient low-rank approximation of SimRank. Journal of Complex Networks, 2016. URL: http://arxiv.org/abs/1410.0717, doi:10.1093/comnet/cnw008. [ bib ]
    15. 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. URL: https://www.eccomas2016.org/proceedings/pdf/10906.pdf. [ bib ]
    16. Igor Ostanin, Ivan Tsybulin, Mikhail Litsarev, Ivan Oseledets, and Denis Zorin. What lies beneath the surface: topological-shape optimization with the kernel-independent fast multipole method. arXiv preprint 1612.04082, 2016. URL: http://arxiv.org/abs/1612.04082. [ bib ]
    17. M. V. Rakhuba and I. V. Oseledets. Grid-based electronic structure calculations: the tensor decomposition approach. J. Comp. Phys., 2016. URL: http://arxiv.org/abs/1508.07632, doi:10.1016/j.jcp.2016.02.023. [ bib ]
    18. Maxim Rakhuba and Ivan Oseledets. Calculating vibrational spectra of molecules using tensor train decomposition. J. Chem. Phys., pages 124101, 2016. doi:10.1063/1.4962420. [ bib ]
    19. Daria A. Sushnikova and Ivan V. Oseledets. “compress and eliminate” solver for symmetric positive definite sparse matrices. arXiv preprint 1603.09133, 2016. URL: http://arxiv.org/abs/1603.09133. [ bib ]
    20. 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. URL: http://arxiv.org/abs/1412.1253, doi:10.1515/rnam-2016-0003. [ bib ]
    21. V. Baranov and I. Oseledets. Fitting high-dimensional potential energy surface using active subspace and tensor train (AS+TT) method. J. Chem. Phys., pages 17107, 2015.doi:10.1063/1.4935017. [ bib ]
    22. 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. doi:10.1515/jiip-2015-0083. [ bib ]
    23. 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.URL: http://arxiv.org/abs/1410.3335, doi:10.1137/140992266. [ bib ]
    24. 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. URL: http://arxiv.org/abs/1403.4068, doi:10.1002/nla.2008. [ bib ]
    25. Christian Lubich, Ivan Oseledets, and Bart Vandereycken. Time integration of tensor trains.SIAM J. Numer. Anal., 53(2):917–941, 2015. URL: http://arxiv.org/abs/1407.2042, doi:10.1137/140976546. [ bib ]
    26. A. Yu. Mikhalev and I. V. Oseledets. Rectangular maximum-volume submatrices and their applications. arXiv preprint 1502.07838, 2015. URL: http://arxiv.org/abs/1502.07838. [ bib ]
    27. I. V. Oseledets, G. V. Ovchinnikov, and A. M. Katrutsa. Linear complexity SimRank using iterative diagonal estimation. arXiv preprint 1502.07167, 2015. URL: http://arxiv.org/abs/1502.07167. [ bib ]
    28. 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.doi:10.2495/BEM380141. [ bib ]
    29. Igor Ostanin, Denis Zorin, and Ivan Oseledets. Toward fast topological-shape optimization. arXiv preprint 1503.02383, 2015. URL: http://arxiv.org/abs/1503.02383. [ bib ]
    30. 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.doi:10.1137/140958529. [ bib ]
    31. 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, 1–3. April 2015. URL: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=7228667&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D7228667. [ bib ]
    32. Ben Usman and Ivan Oseledets. Tensor SimRank for heterogeneous information networks. arXiv preprint 1502.06818, 2015. URL: http://arxiv.org/abs/1502.06818. [ bib ]
    33. 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. URL: http://arxiv.org/abs/1407.3023, doi:10.1109/TCAD.2014.2369505. [ bib ]
    34. P. -A. Absil and I. V. Oseledets. Low-rank retractions: a survey and new results. Comput. Optim. Appl., 2014. URL: http://sites.uclouvain.be/absil/2013.04, doi:10.1007/s10589-014-9714-4. [ bib ]
    35. 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. doi:10.1016/j.camwa.2014.03.002. [ bib ]
    36. 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. doi:10.1016/j.compchemeng.2013.10.020. [ bib ]
    37. 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. doi:10.1016/j.cpc.2013.12.017. [ bib ]
    38. Vadim Lebedev, Yaroslav Ganin, Maxim Rakhuba, Ivan Oseledets, and Victor Lempitsky. Speeding up convolutional neural networks using fine-tuned CP-decomposition. arXiv preprint 1412.6553, 2014. URL: http://arxiv.org/abs/1412.6553. [ bib ]
    39. 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.doi:10.1016/j.cpc.2014.06.012. [ bib ]
    40. Christian Lubich and Ivan V. Oseledets. A projector-splitting integrator for dynamical low-rank approximation. BIT, 54(1):171–188, 2014. doi:10.1007/s10543-013-0454-0. [ bib ]
    41. Ivan Oseledets. Solving high-dimensional problems via stable and efficient tensor factorization techniques. In Abstracts of Papers of the American Chemical Society, volume 246, 244–Phys. 2014. [ bib ]
    42. G. V. Ovchinnikov, D. A. Kolesnikov, and I. V. Oseledets. Algebraic reputation model RepRank and its application to spambot detection. arXiv preprint 1411.5995, 2014. URL: http://arxiv.org/abs/1411.5995. [ bib ]
    43. Vladimir A. Kazeev and Ivan V. Oseledets. The tensor structure of a class of adaptive algebraic wavelet transforms. Preprint 2013-28, ETH SAM, Z\”urich, 2013. URL: http://www.sam.math.ethz.ch/sam_reports/reports_final/reports2013/2013-28.pdf. [ bib ]
    44. Vladimir Lyashev, Ivan Oseledets, and Delai Zheng. Tensor-based multiuser detection and intra-cell interference mitigation in LTE PUCCH. In Proc. TELFOR 2013, 385–388. 2013.doi:10.1109/TELFOR.2013.6716250. [ bib ]
    45. E.A. Muravleva and I.V. Oseledets. Fast low-rank solution of the Poisson equation with application to the Stokes problem. arXiv preprint 1306.2150, 2013. URL: http://arxiv.org/abs/1306.2150. [ bib ]
    46. I. V. Oseledets. Constructive representation of functions in low-rank tensor formats. Constr. Approx., 37(1):1–18, 2013. doi:10.1007/s00365-012-9175-x. [ bib ]
    47. S. V. Dolgov, Boris N. Khoromskij, and Ivan 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.doi:10.1137/120864210. [ bib ]
    48. 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. URL: http://www.mis.mpg.de/de/publications/preprints/2011/prepr2011-12.html, doi:10.4208/jcm.1110-m11si08. [ bib ]
    49. 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. doi:10.1016/j.laa.2011.09.010. [ bib ]
    50. 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. doi:10.1137/100792056. [ bib ]
    51. 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. doi:10.1137/110833142. [ bib ]
    52. I. V. Oseledets, B. N. Khoromskij, and R. Schneider. Efficient time-stepping scheme for dynamics on TT-manifolds. Preprint 24, MPI MIS, 2012. URL: http://www.mis.mpg.de/preprints/2012/preprint2012_24.pdf. [ bib ]
    53. 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.doi:10.1016/j.laa.2011.07.041. [ bib ]
    54. 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.doi:10.1515/rjnamm.2011.017. [ bib ]
    55. I. V. Oseledets. Improved n-term Karatsuba-like formulas in GF(2). IEEE Trans. Computers, 60(8):1212–1216, 2011. doi:10.1109/TC.2010.233. [ bib ]
    56. 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. doi:10.1109/nDS.2011.6076872. [ bib ]
    57. I. V. Oseledets. Tensor-train decomposition. SIAM J. Sci. Comput., 33(5):2295–2317, 2011.doi:10.1137/090752286. [ bib ]
    58. I. V. Oseledets. DMRG approach to fast linear algebra in the TT–format. Comput. Meth. Appl. Math., 11(3):382–393, 2011. doi:10.2478/cmam-2011-0021. [ bib ]
    59. I. V. Oseledets and E. E. Tyrtyshnikov. Algebraic wavelet transform via quantics tensor train decomposition. SIAM J. Sci. Comput., 33(3):1315–1328, 2011. doi:10.1137/100811647. [ bib ]
    60. 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. [ bib ]
    61. 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. doi:10.1109/nDS.2011.6076873. [ bib ]
    62. S. Dolgov, B. Khoromskij, I. V. Oseledets, and E. E. Tyrtyshnikov. Tensor structured iterative solution of elliptic problems with jumping coefficients. Preprint 55, MPI MIS, Leipzig, 2010. URL: http://www.mis.mpg.de/preprints/2010/preprint2010_55.pdf. [ bib ]
    63. S. A. Goreinov, I. V. Oseledets, D. V. Savostyanov, E. E. Tyrtyshnikov, and N. L. Zamarashkin. How to find a good submatrix. In V. Olshevsky and E. Tyrtyshnikov, editors, Matrix Methods: Theory, Algorithms, Applications, pages 247–256. World Scientific, Hackensack, NY, 2010. [ bib ]
    64. B. N. Khoromskij and I. V. Oseledets. DMRG+QTT approach to computation of the ground state for the molecular Schr\”odinger operator. Preprint 69, MPI MIS, Leipzig, 2010. URL: http://www.mis.mpg.de/preprints/2010/preprint2010_69.pdf. [ bib ]
    65. 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.doi:10.2478/cmam-2010-0023. [ bib ]
    66. I. V. Oseledets. Approximation of $2^d \times 2^d$ matrices using tensor decomposition. SIAM J. Matrix Anal. Appl., 31(4):2130–2145, 2010. doi:10.1137/090757861. [ bib ]
    67. 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.doi:10.1016/j.laa.2009.11.010. [ bib ]
    68. 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.doi:10.1002/nla.682. [ bib ]
    69. I. V. Oseledets and E. E. Tyrtyshnikov. TT-cross approximation for multidimensional arrays.Linear Algebra Appl., 432(1):70–88, 2010. doi:10.1016/j.laa.2009.07.024. [ bib ]
    70. I. V. Oseledets. A new tensor decomposition. Doklady Math., 80(1):495–496, 2009.doi:10.1134/S1064562409040115. [ bib ]
    71. I. V. Oseledets. Approximation of matrices with logarithmic number of parameters. Doklady Math., 428(1):23–24, 2009. doi:10.1134/S1064562409050056. [ bib ]
    72. 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.doi:10.1137/060650738. [ bib ]
    73. I. V. Oseledets, D. V. Savostyanov, and E. E. Tyrtyshnikov. Linear algebra for tensor problems.Computing, 85(3):169–188, 2009. doi:10.1007/s00607-009-0047-6. [ bib ]
    74. 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.doi:10.1134/S0965542509020092. [ bib ]
    75. 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. doi:10.1137/090748330. [ bib ]
    76. I. V. Oseledets and E. E. Tyrtyshnikov. Recursive decomposition of multidimensional tensors.Doklady Math., 427(1):14–16, 2009. doi:10.1134/S1064562409040036. [ bib ]
    77. I. V. Oseledets and E. E. Tyrtyshnikov. Tensor tree decomposition does not need a tree. Preprint (Submitted to Linear Algebra Appl) 2009-04, INM RAS, Moscow, 2009. URL: http://pub.inm.ras.ru/pub/inmras2009-08.pdf. [ bib ]
    78. 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.doi:10.1016/j.laa.2009.03.001. [ bib ]
    79. 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. doi:10.1134/S106456240905010X. [ bib ]
    80. 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. doi:10.1007/978-3-7643-8539-2_14. [ bib ]
    81. I. V. Oseledets. Optimal Karatsuba-like formulae for certain bilinear forms in GF(2). Linear Algebra Appl., 429(8):2052–2066, 2008. doi:10.1016/j.laa.2008.06.004. [ bib ]
    82. I. V. Oseledets. The integral operator with logarithmic kernel has only one positive eigenvalue.Linear Algebra Appl., 428(7):1560–1564, 2008. [ bib ]
    83. 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.doi:10.1137/060655894. [ bib ]
    84. I. V. Oseledets. Lower bounds for separable approximations of the Hilbert kernel. Mat. Sb., 198(3):137–144, 2007. [ bib ]
    85. 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. doi:10.1016/j.laa.2005.03.040. [ bib ]
    86. I. V. Oseledets and D. V. Savostyanov. Minimization methods for approximating tensors and their comparison. Comput. Math. Math. Phys., 46(10):1641–1650, 2006.doi:10.1134/S0965542506100022. [ bib ]
    87. 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. [ bib ]
    88. 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. [ bib ]
    89. 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. [ bib ]
    90. 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. [ bib ]
    91. 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. doi:10.1515/156939804323089334. [ bib ]
  • 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)
Raghavendra Belur Jana
Senior Research Scientist
Alexey Boyko
Alexey Boyko
PhD student
Andrey Chertkov
PhD student
Evgenii Frolov
PhD student
Alexey Golovizin
Research Engineer
Julia (Yulia) Gusak
Research Scientist
Sergey Matveev
Research Scientist
Leyla Mirvakhabova
PhD student
Igor Ostanin
Research Scientist
George Ovchinnikov
Research Scientist
Anna Petrovskaia
PhD student
Gleb Ryzhakov
Research Scientist
Talgat Daulbaev
PhD student
Daniil Merkulov
PhD student

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