Graduated from Moscow State University (1972).

PhD thesis (advised by A.Yu.Levin): “Fast algorithms for constructing minimax subgraphs”(1978).

Doctorate thesis: “Optimization of the average queue length in computing systems”.

Affiliation:

1972 to present — works for the Yaroslavl State University, at a present a professor.

Part-time work:

Kolmogorov Special Science Education Center № 18 — 1969-1972.

Institute of Computer Science Problem — 1992-1998, Senior Research.

Science interests and results.

1. Fast algorithms for combinatorial problems: k-connectivity, optimization on a permutation polyhedra and their generalizations.

2. Probabilistic extremal problems on graphs: a minimum spanning tree and a traveling salesman.

3. Optimization problems of queuing theory: to reduce the problem of optimizing the functions of the average queue length to an optimization problem in a special polyhedron.

4. Models of neurons that receive information from the frequency of input streams.

5. Estimating the dimensions by the experimental data: the construction of nonparametric estimators, finding the conditions for convergence and efficiency in certain metric spaces.

PhD thesis (advised by A.Yu.Levin): “Fast algorithms for constructing minimax subgraphs”(1978).

Doctorate thesis: “Optimization of the average queue length in computing systems”.

Affiliation:

1972 to present — works for the Yaroslavl State University, at a present a professor.

Part-time work:

Kolmogorov Special Science Education Center № 18 — 1969-1972.

Institute of Computer Science Problem — 1992-1998, Senior Research.

Science interests and results.

1. Fast algorithms for combinatorial problems: k-connectivity, optimization on a permutation polyhedra and their generalizations.

2. Probabilistic extremal problems on graphs: a minimum spanning tree and a traveling salesman.

3. Optimization problems of queuing theory: to reduce the problem of optimizing the functions of the average queue length to an optimization problem in a special polyhedron.

4. Models of neurons that receive information from the frequency of input streams.

5. Estimating the dimensions by the experimental data: the construction of nonparametric estimators, finding the conditions for convergence and efficiency in certain metric spaces.