2 professors and doctors of sciences, 4 associate professors and candidates of sciences
The employees of the department read 35 normative and special courses
The Department of Applied Statistics was established on June 7, 1978
Full name: | Office | Research interests |
---|---|---|
Prof. Pavlo S. Knopov | 709 | statistical decision theory, methods of stochastic optimization, theory of optimal control of stochastic systems |
Prof. Mykhaylo M. Savchuk | 709 | probabilistic combinatorics, mathematical methods of information security, cryptography and cryptoanalysis, applied statistics |
Assoc. Prof. Alexander S. Slabospitsky | 706 б | estimation theory, data analysis, applied statistics, regression analysis, analysis of variance, correlation analysis, system identification theory, parameter/state estimation of technical objects, recurrent algorithms of system identification, set membership estimation, real-time data processing, system modelling, asymptotic analysis of estimation and identification algorithms, software for analysis and processing of data, computer networks, Internet-technologies |
Assoc. Prof. Michael M. Sharapov | 706 б | limiting theorems for estimates of parameters of random processes and fields with long memory, modeling of random processes and fields with long memory, random walks on graphs, combinatorial solutions of linear programming task, computer realizations of statistical algorithms |
Assoc. Prof. Irina V. Rozora | 709 | property investigation of stochastic processes and fields, modelling of random processes with given accuracy and reliability in various Banach spaces, actuarial mathematics |
Assist. Prof. Igor A. Makushenko | 709 | investigation of stochastic processes of queueing theory, asymptotic analyses of compound stochastic systems, statistical prediction methods |
Assist. Prof. Anna V. Livinska | 709 | random processes, queueing models, asymptotic analysis of stochastic systems |
Prof. Natalia V. Semenova | 709 | development and research models and methods of discrete optimization, as well as problems with ambiguous data set, stability analysis of vector discrete optimization problems. |