Mikhail Zaslavskiy

研究:

教授essor Zaslavskiy's scientific interests mostly lie in the fields of model reduction, 线性代数, approximation theory, multi-scale methods, homogenization with application to solution of forward and inverse PDE problems. While most of the numerical approaches he's developed were primarily targeted to geophysical exploration, he is constantly looking for other areas of applications.  In particular, dueto much in common in the nature of measurements, he has started exploring applications in radar imaging, medical imaging as well propellant tank imaging for NASA applications. Also, he is currently investigating the natural extension of his developments to the area of data science and unsupervised machine learning. A wide range of applications here includes bioinformatics, 地球物理学, social network studies, financial mathematics, 等. 准确地说, within the broad area of topics above, four of them represent particular areas of interest.

1. 1. Model-driven reduced-order models for fast solution of large-scale diffusive and wave forward problems.  In particular, 教授. Zaslavskiy是interested in the study of approximation properties of variousKrylov subspace projection-based model reduction as well as strategies for selecting optimal or near-optimal ROMs. The applications here include forward electromagnetic, acoustic and seismic exploration.
   2. Multi-scale ROM methods and homogenization for discretization for large-scale elliptic, diffusive and wave forward PDEs. That includes the development of低阶as well as spectral multi-scale discretization methods on coarse grids non-conforming to media structures. The latter ones are needed when too coarse cells are considered,而且,therefore the introduction of dispersive properties for such cells is required.
   3. Data-driven reduced-order models for solving nonlinear wave and diffusive inverse PDE problems. Here he is interested in mapping the given data to the specific types of ROM mimicking the structure of sparse dynamical system. The parameters of such ROMs have a physical meaning of unknown inverse problem unknowns, so one can either interpret them directly or use such map as preconditioner for iterative inversion algorithm. Another application of such ROM is so-called Data-to-Born transform that allows to remove nonlinearity of inverse PDE problems.
   4. Unsupervised machine learning for 聚类 of large datasets. This is the most recent topic of 教授. Zaslavskiy's research, so it is in active development at this point. 它的终极目标would be the development of a divide-and-conquer type of algorithm for spectral data 聚类.In particular, firstthe data is split into subsets and then each subset is集群独立. The final step would be聚类of all the clusters obtained for all data subsets. It turned out that data subset 聚类 can be efficiently performed in the model reduction framework. At the current stage he is exploring this intimate connection in细节,我.e. constructing optimal reduced-order models (ROMs), investigating various algorithms for ROM 聚类, 等. 范围of data sources he has applied the subset 聚类 for includes stock market statistics, social network connections, collaboration networks, well logging as well as gene expressions.

教授. Zaslavskiy strongly believes that a good sign of novel theoretical development is that it was originally driven by application in mind. And, vice versa, it is unlikely to get a breakthrough in applications without new solid theory. 因此, he constantly tries to keep tight connections with his current collaborators from both academia and industry as well as to explore new areas and ideas for research.