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Statistical Machine Learning Methods for Complex Data Sets

The main goal of the present project is the development of new statistical methods and data analytics for complex data sets.

This includes different objectives related to the multifacet structure of the Data Sets:
- Investigating statistical models for ranking data and random combinatorial structures. This include development of novel statistical tests and computing asymptotic efficiency and robustness of rank tests. New probability models for ranking data will be investigated using distances on permutations and coset sets of permutations.
- Development of new methods for Data Analytics for Complex Structured Data sets, including methods of Optimization and nonparametric regressions, Harmonic Analysis and Wavelets on Networks.
- Development of Data Analytics applications for the for achine and Deep Learning on structured Big Data, including application of network analysis for genetic data, astroinformatics, and Mathematical Finance.

The main methodology of this project consists of mathematical and statistical methods, related to Complex Data Sets. The project team members are qualified in appropriated mathematical and statistical methods that will be used to achieve the project goals. In our study, we will derive novel methods for finding solutions of these problems and will also apply some existing ones.