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Faculty of Mathematics

 

Dr Yury Korolev

 

Career

  • Apr 2020 - present: CCIMI Fellow, University of Cambridge
  • Apr 2018 - Mar 2020: Royal Society Newton International Fellow, University of Cambridge
  • Oct 2017 - Mar 2018: Humboldt Fellow, University of Muenster
  • Jan 2017 - Sep 2017: Postdoc, University of Luebeck
  • Jan 2014 - Dec 2016: Postdoc, Queen Mary University of London
  • Mar 2013 - Dec 2013: Instructor, Moscow State University
  • Mar 2010 - Mar 2013: PhD student, Moscow State University

Research Interests

Inverse Problems, Variational Methods, Imaging, Data science

Teaching

I am teaching Inverse Problems together with Hanne Kekkonen in Michaelmas term 2019/2020.

I taught Inverse Problems in Imaging in Michaelmas term 2018/2019.

Awards

  • 2018 Royal Society Newton International Fellowship (2 years)
  • 2018 Research Fellowship of Hughes Hall, University of Cambridge (3 years)
  • 2017 Humboldt Research Fellowship for Postdoctoral Researchers (2 years)
  • 2009 Tikhonov scholarship for excellent academic and research results (1 year)
  • 2007 Potanin scholarship for excellent academic results and leadership skills (1 year)

Publications

  1. L. Bungert, M. Burger, Y. Korolev, C.-B. Schoenlieb (2020). Variational regularisation for inverse problems with imperfect forward operators and general noise models
    arXiv: 2005.14131
  2. T. Grossmann, Y. Korolev, G. Gilboa, C.-B. Schönlieb (2020). Deeply Learned Spectral Total Variation Decomposition
    arXiv: 2006.10004
  3. L. Bungert, Y. Korolev, M. Burger (2020). Structural analysis of an L-infinity variational problem and relations to distance functions // Pure and Applied Analysis (accepted)
    arXiv: 2001.07411
  4. A. Aspri, Y. Korolev, O. Scherzer (2019). Data driven regularization by projection
    arXiv:1909.11570
  5. M. Burger, Y. Korolev, S. Parisotto, C. Schönlieb (2019). Total Variation Regularisation with Spatially Variable Lipschitz Constraints
    arXiv: 1912.02768
  6. M. Burger, Y. Korolev, J. Rasch (2019). Convergence rates and structure of solutions of inverse problems with imperfect forward models // Inverse Problems, Special Issue on Variational Methods and Effective Algorithms for Imaging and Vision, 35(2) 024006
    DOI: 10.1088/1361-6420/aaf6f5; arXiv: 1806.10038
  7. M. Burger, Y. Korolev, C. Schönlieb, C. Stollenwerk (2019). A total variation based regularizer promoting piecewise-Lipschitz reconstructions // Proceedings of the 7th International Conference on Scale Space and Variational Methods in Computer Vision, Springer
    arXiv: 1903.05079
  8. Y. Korolev, J. Lellmann (2018). Image reconstruction with imperfect forward models and applications in deblurring // SIAM Journal on Imaging Sciences, 11(1), 197-218
    DOI: 10.1137/17M1141965; arXiv: 1708.01244
  9. V. Toropov, Y. Korolev, K. Barkalov, E. Kozinov, V. Gergel (2018). HPC Implementation of the Multipoint Approximation Method for Large Scale Design Optimization Problems Under Uncertainty // Proceedings of the 6th International Conference on Engineering Optimization, Springer
    DOI: 10.1007/978-3-319-97773-7_27
  10. Y. Korolev, V. Toropov, S. Shahpar (2017). Design Optimization Under Uncertainty Using the Multipoint Approximation Method // Proceedings of the 19th AIAA Non-Deterministic Approaches Conference, Grapevine TX 
    DOI: 10.2514/6.2017-1934
  11. A. Gorokh, Y. Korolev, T. Valkonen (2016). Diffusion tensor imaging with deterministic error bounds // Journal of Mathematical Imaging and Vision, 56(1), 137-157
    DOI: 10.1007/s10851-016-0639-7; arXiv: arXiv:1509.02223
  12. Y. Korolev, V. Toropov, S. Shahpar (2015). Large-scale CFD Optimisation based on the FFD Parametrisation using the Multipoint Approximation Method in an HPC Environment // Proceedings of the 16th AIAA/ISSMO Multidisciplinary Analysis and Optimisation Conference, Dallas TX
    DOI: 10.2514/6.2015-3234
  13. Y. Korolev, S. Karabasov, V. Toropov (2015). Automatic Optimizer vs Human Optimizer for Low-Order Jet Noise Modelling // Proceedings of the 21st AIAA/CEAS Aeroacoustics Conference, Dallas TX
    DOI: 10.2514/6.2015-2215
  14. Y. Korolev, V. Toropov (2015). The Multipoint Approximation Method as a parallel optimisation framework for problems with computationally expensive responses // Proceedings of the 4th International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering, Dubrovnik
    DOI: 10.4203/ccp.107.3
  15. Y. Korolev (2014). Making use of partial order in solving inverse problems: II // Inverse Problems, 30(8), 085003
    DOI: 10.1088/0266-5611/30/8/085003
  16. Y. Korolev, A. Yagola (2013). Making use of partial order in solving inverse problems // Inverse Problems, 29(9), 095012
    DOI: 10.1088/0266-5611/29/9/095012
  17. A. G. Yagola and Y. M. Korolev (2013). Error estimation in ill-posed problems in special cases // Applied Inverse Problems. Vol. 48 of Springer Proceedings in Mathematics & Statistics. Springer, New York, p. 155–164
    DOI: 10.1007/978-1-4614-7816-4_9
  18. Y. Korolev, A. Yagola, J. Johnson, D. Brinkerhoff (2013). Methods of error estimation in inverse problems on compact sets in Banach lattices – theory and applications in ice sheet modeling // Proceedings of the 4th Inverse Problems, Design and Optimisation symposium, Albi
    HAL: 01440841
  19. Y. Korolev, A. Yagola (2012). On inverse problems in partially ordered spaces with a priori information // Journal of Inverse and Ill-posed Problems, 20(4), pp. 567-573
    DOI: 10.1515/jip-2012-0022
  20. Y. Korolev, H. Kubo, A. Yagola (2012). Parameter identification problem for a parabolic equation – application to the Black–Scholes option pricing model // Journal of Inverse and Ill-posed Problems, 20(3), pp. 327-337
    DOI: 10.1515/jip-2012-0043
  21. Y. Korolev, A. Yagola (2012). Error estimation in linear ill-posed problems with prior information // Computational methods and programming, vol. 13, pp. 14-18 (in Russian)
  22. A. Yagola, Y. Korolev (2012). Error estimations in linear inverse problems in ordered spaces // Proceedings of the 8th Congress of the International Society for Analysis, its Applications, and Computations, vol. 2. Peoples’ Friendship University of Russia, Moscow
  23. A. Yagola, Y. Korolev (2011). Error estimations in linear inverse problems with a priori information // Proceedings of the International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE, Washington DC
    DOI: 10.1115/DETC2011-47799
  24. Y. Korolev, P. Golubtsov (2010). Two-level competition systems in common resource management problems // Mathematical game theory and applications, 2(4), pp. 25-51 (in Russian)
  25. Y. Korolev, P. Golubtsov (2009). Modelling of common resource management problems // Proceedings of the “Lomonosov readings” conference, Moscow (in Russian)

    Publications

    Variational regularisation for inverse problems with imperfect forward operators and general noise models.
    L Bungert, M Burger, Y Korolev, C-B Schoenlieb
    – CoRR
    (2020)
    abs/2005.14131,
    Convergence rates and structure of solutions of inverse problems with imperfect forward models
    M Burger, Y Korolev, J Rasch
    – Inverse Problems
    (2019)
    35,
    024006
    (DOI: 10.1088/1361-6420/aaf6f5)
    A Total Variation Based Regularizer Promoting Piecewise-Lipschitz Reconstructions
    M Burger, Y Korolev, CB Schönlieb, C Stollenwerk
    – Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    (2019)
    11603 LNCS,
    485
    (DOI: 10.1007/978-3-030-22368-7_38)
    Total Variation Regularisation with Spatially Variable Lipschitz Constraints.
    M Burger, Y Korolev, S Parisotto, C-B Schönlieb
    – CoRR
    (2019)
    abs/1912.02768,
    HPC Implementation of the Multipoint Approximation Method for Large Scale Design Optimization Problems Under Uncertainty
    V Toropov, Y Korolev, K Barkalov, E Kozinov, V Gergel
    (2019)
    296
    (DOI: 10.1007/978-3-319-97773-7_27)
    Image Reconstruction with Imperfect Forward Models and Applications in Deblurring
    Y Korolev, J Lellmann
    – SIAM Journal on Imaging Sciences
    (2018)
    11,
    197
    (DOI: 10.1137/17m1141965)
    Design Optimization Under Uncertainty Using the Multipoint Approximation Method
    YM Korolev, VV Toropov, S Shahpar
    – 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 2017
    (2017)
    (DOI: 10.2514/6.2017-1934)
    Diffusion Tensor Imaging with Deterministic Error Bounds
    A Gorokh, Y Korolev, T Valkonen
    – Journal of Mathematical Imaging and Vision
    (2016)
    56,
    137
    (DOI: 10.1007/s10851-016-0639-7)
    Automatic optimizer vs human optimizer for low-order jet noise modelling
    YM Korolev, SA Karabasov, VV Toropov
    – 21st AIAA/CEAS Aeroacoustics Conference
    (2015)
    (DOI: 10.2514/6.2015-2215)
    Large-scale CFD Optimization based on the FFD Parametrization using the Multipoint Approximation Method in an HPC Environment
    YM Korolev, VV Toropov, S Shahpar
    – 16th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference
    (2015)
    (DOI: 10.2514/6.2015-3234)
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    Research Groups

    Cambridge Image Analysis
    Cantab Capital Institute for the Mathematics of Information

    Email

    y.korolev@maths.cam.ac.uk

    Room

    F2.07

    Telephone

    01223 337892

    Study at Cambridge

    About the University

    Research at Cambridge