Master Program CV

Course title: Operations research and theory of games

Course developer: prof L.A. Petrosjan

Course hours: lectures – 36 hours, seminars – 34 hours, unassisted work – 60 hours



Course title: Game-theoretic models of economic competition

Course developer: Dr. Kuzyutin D.V.

Course hours: lectures – 18 hours, seminars – 14 hours, unassisted work – 49 hours



Course title: Applied Statistics in R

Course developer: Dr. E.M. Parilina

Course hours: lectures – 18 hours, laboratory works – 16 hours, unassisted work – 66 hours



Course title: Computer science

Course developer: Dr. T.A. Lepikhin

Course hours: seminars – 16 hours, unassisted work – 32 hours



Course title: Financial mathematics

Course developer: Dr. S. Sh. Kumacheva

Course hours: lectures – 18 hours, seminars – 16 hours, unassisted work – 34 hours



Course title: Graphs and networks

Course developer: Tur Anna

Course hours: lectures – 16 hours, seminars – 18 hours, unassisted work – 34 hours



Course title: Inventory management

Course developer: Dr.A.A. Sedakov

Course hours: lectures – 18 hours, seminars – 14 hours, unassisted work – 34 hours


The Master course on Inventory Management focuses on advanced chapters of Operations Research which arise in the logistics sector, and aims at teaching the basic principles of inventory management and logistics. Because of its practical importance, inventory theory focuses on both deterministic and stochastic models. During the course, students are taught to find an appropriate inventory policy for either of these two types of models.

The primary objectives of this course are: a general introduction to inventory management and consideration of concepts, models and algorithms from Operational Research and Statistics for inventory control. This is a reason that applicants are required to have knowledge of Calculus, Probability Theory and Statistics. Graduate students will learn how to apply quantitative techniques, methods and models to real-world logistics problems and solve these problems in practice without any assistance.


Course title: Computer simulation and queue management

Course developer: Dr. A. M. Kovshov

Course hours: lectures – 18 hours, seminars – 14 hours, laboratory works – 2 hours, unassisted work – 34 hours



Course title: Mathematical methods in forecast and prediction

Course developer: Dr. S.I. Tarashnina

Course hours: lectures – 18 hours, seminars – 16 hours, unassisted work – 34 hours



Course title: Queueing theory.

Course developer: Sergey Kostyunin.

Course hours: lectures – 18 hours, seminars – 16 hours, unassisted work – 54 hours

Learning outcomes

The course gives a basic knowledge in modeling and analysis of queuing.

After completion of the course the student should be able to:

Define and explain basic concepts in the theory Markov processes, M/M/m, M/M/m/K, and M/M/m/K/C queuing systems.
Derive and apply main formulas for some properties (such as stationary probabilities, average waiting and system time, expected number of customers in the queue, etc.) of M/M/m, M/M/m/K, and M/M/m/K/C queuing systems.
Calculate the traffic intensity, blocked traffic, and the utilization of some queuing systems..
Solve some simple problems on queuing networks.
Course main content

This course includes the classical theory for queuing systems:

Basic terminology, Kendall’s notation and Little’s theorem.
Discrete and continuous time Markov chains, birth-death processes, and the Poisson process.
Markovian waiting systems with one or more servers, and systems with infinite as well as finite buffers and finite user populations (M/M/).
Systems with general service distributions (M/G/1): the method of stages, Pollaczek-Khinchin mean-value formula and systems with priority and interrupted service.
Loss systems according to Erlang, Engset and Bernoulli.
Open and closed queuing networks, Jackson networks.


Course title: Mathematical models of revenue management

Course developer: Dr. Fedor Nikitin

Course hours: seminars – 18 hours, unassisted work – 51 hours


Revenue Management is practical discipline of usage operational research techniques to make correct demand management decision in competitive environment. Started in late 70s as a set of obscure practices among airlines it has grown nowdays to the big area of management science with many industry adopters.

Main goal of the course is to introduce students to mathematical methods behind Revenue Management practices, demonstrate their usage on real-life examples and give hands-on experience in implementation of Revenue Manage-ment systems.

Course consists of theoretical and practical parts. Theoretical part provides understanding of mathematical modelling of consumer behaviour, forecasting and optimization methods used in Revenue Management. Practical part is devoted to aspects of implementation of theoretical models leading to computational algorithms.


Course title: Statistical decisions and econometrics

Course developer: prof. V.M. Bure

Course hours: seminars – 16 hours, unassisted work – 30 hours


The course contains theory of applied statistics and regression analysis for econometrics, and explains applications of different branches of applied statistics to a variety of econometric problems and types of data: Hypothesis testing and confidence intervals; Linear regression with one or more regressors; Discriminate analysis; Analysis of variations; Principal components analysis; Logistic regression; Median and quantile regressions; A bit of time series analysis. Students will also be introduced to statistical computing with Excel and other packets.

Knowledge outcomes
The course builds on a first course in statistics, and gives broad knowledge of many aspects of applied statistics relevant and useful for econometric studies, as listed above. The course focuses on modeling and economic applications. The successful student will have insight and knowledge to understand much of the applied econometric literature, and to do econometric analyses. Much time are devoted to practical working with statistical modeling.

Excel is a widespread program for statistical and econometric computation. Basic skills in using Excel and other packets in performing various applied statistics analyses on economically interesting data will be developed through exercises, and through study of the practical examples.


Course title: Time series analysis

Course developer: Dr. Ekaterina Shevkoplyas

Course hours: lectures –18 hours, seminars – 14 hours, unassisted work – 34 hours

Course Description

The course provides a survey of the theory and application of time series methods in econometrics. Topics covered will include univariate stationary and non-stationary models, vector autoregressions, frequency domain methods, models for estimation and inference in persistent time series, and structural breaks. We will cover different methods of estimation and inferences of modern dynamic stochastic general equilibrium models (DSGE): simulated method of moments, maximum likelihood and Bayesian approach. The empirical applications in the course will be drawn primarily from macroeconomics.


The main objective of this course is to develop the skills needed to do empirical research in fields operating with time series data sets. The course aims to provide students with techniques and receipts for estimation and assessment of quality of economic models with time series data. Special attention will be placed on limitations and pitfalls of different methods and their potential fixes. The course will also emphasize recent developments in time series analysis and will present some open questions and areas of ongoing research.


Course title: Finite dimensional optimization methods

Course developer: prof. L.N. Polyakova

Course hours: lectures – 16 hours, seminars – 16 hours, unassisted work – 34 hours



Course title: Project management

Course developer: Dr. E.A. Gubar

Course hours: lectures – 18 hours, seminars – 12 hours, unassisted work – 54 hours



Course title: Modern control theory

Course developer: prof. V.L. Kharitonov

Course hours: lectures – 36 hours, seminars– 34 hours, unassisted work –60 hours



Course title: Random processes

Course developer: Akimova A.N.

Course hours: lectures – 18 hours, seminars – 16 hours, unassisted work – 34 hours



Course title: Dynamic games

Course developer: Dr.A.A. Sedakov, Dr. E.M.Parilina, Dr. E.A. Gubar,

Course hours: lectures – 18 hours, seminars – 14 hours, unassisted work – 49 hours

Dynamic Games

Dynamic Games course is recommended for master students who are interested in topics of Game Theory and Operations Research. Students are required to have basic knowledge of Calculus, Theory of Ordinary Differential Equations, Probability Theory and Game Theory.

The first module of the Dynamic Games course introduces master students to a theory of extensive form games. In this part we are limited to the case of games with perfect information. Well-known concepts of subgame-perfectness and punishment are considered. In a cooperative case of the extensive form games we face the problem of time-inconsistency of the solution which never happens in static, non-dynamic cases. To overcome this problem, the imputation distribution procedure is proposed.