Nikolay Yavich is an applied mathematician with expertise in several areas including large-scale numerical modeling, high-performance computing, fast solvers, inverse problems, computational geophysics, and biomedical imaging.
Nikolay got his Ph.D. from the University of Houston focused on preconditioned iterative solvers for anisotropic problems. He is currently a senior research scientist at Skoltech working on novel approaches to EEG and MEG data modeling and imaging as well as application of a new generation of MEG hardware.
Novel approaches to electroencephalography (EEG) and magnetoencephalography (MEG) data modeling and imaging as well as application of a new generation of MEG hardware.
* N.Yavich, N. Khokhlov, M. Malovichko, M. Zhdanov, Contraction Operator Transformation for the complex Heterogeneous Helmholtz Equation, Computers and Mathematics with Applications, 86 (2021) 63–72, doi.org/10.1016/j.camwa.2021.01.018
* A. Razorenova, N. Yavich, M. Malovichko, M. Fedorov, N. Koshev, and D. V. Dylov, Deep learning for non-invasive cortical potential imaging, in MLCN workshop MICCAI, 2020 doi.org/10.1101/2020.06.15.151480
* N.Koshev, N.Yavich, M Malovichko, E.Skidchenko, M.Fedorov, FEM-based Scalp-to-Cortex EEG data mapping via the solution of the Cauchy problem Journal of Inverse and Ill-posed Problems, 28(4), 2020 doi.org/10.1515/jiip-2019-0065
* N.Yavich, M.Zhdanov, Finite-element EM modelling on hexahedral grids with an FD solver as a pre-conditioner, Geophysical Journal International 223 (2), 840-850, 2020 doi.org/10.1093/gji/ggaa341