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Faculty of Mathematics



  • Apr 2018 - date: 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


I am a Newton International Fellow at the Department of Applied Mathematics and Theoretical Physics and a member of the Cambridge Image Analysis group. I am also a Fellow of Hughes Hall, University of Cambridge. I am interested in inverse problems, variational regularisation and imaging science. Broadly speaking, inverse problems deal with extracting information from indirectly measured data using models that describe the data acquisition and arise, for instance, in medical imaging, astronomy, microscopy and geoscience. I am interested in cases when mathematical models of real-world phenomena are either imprecise, computationally too expensive or not available at all and are only given through training data. I develop regularisation theory and algorithms in this context and use them in imaging applications.


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.


2018 Research Fellowship of Hughes Hall, University of Cambridge (3 years)
2017 Newton International Fellowship (2 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)


  1. M. Burger, Y. Korolev, S. Parisotto, C. Schönlieb (2019). Total Variation Regularisation with Spatially Variable Lipschitz Constraints // / arXiv:1912.02768
  2. A. Aspri, Y. Korolev, O. Scherzer (2019). Data driven regularization by projection // arXiv:1909.11570
  3. 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
  4. 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, Hofgeismar
  5. Y. Korolev, J. Lellmann (2018). Image reconstruction with imperfect forward models and applications in deblurring // SIAM Journal on Imaging Sciences, 11(1), 197-218
  6. 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
  7. A. Gorokh, Y. Korolev, T. Valkonen (2016). Diffusion tensor imaging with deterministic error bounds // Journal of Mathematical Imaging and Vision, 56(1), 137-157
  8. 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
  9. 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
  10. 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
  11. Y. Korolev (2014). Making use of partial order in solving inverse problems: II // Inverse Problems, 30(8), 085003
  12. Y. Korolev, A. Yagola (2013). Making use of partial order in solving inverse problems // Inverse Problems, 29(9), 095012
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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)
  18. 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
  19. 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
  20. 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)
  21. Y. Korolev, P. Golubtsov (2009). Modelling of common resource management problems // Proceedings of the “Lomonosov readings” conference, Moscow (in Russian)

Research Groups

Cambridge Image Analysis
Cantab Capital Institute for the Mathematics of Information




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