Publications
 Proceedings of the 13th International Conference on Machine Learning and Applications (ICMLA'14), pp. 350355,
 Proceedings of the 2017 ACM SIGPLAN Workshop on Partial Evaluation and Program Manipulation,

Computational Stochastic Modeling of Cellular Microtubule Network46th Annual Meeting of the American Society for Cell Biology,

Computersupported collaborative learning with mindmapsCommunications in Computer and Information Science 17 CCIS, pp. 478489,
 Proceedings of International Conference on Algorithms for Computational Biology.  2015.  141153,

GRADESNDA '18 Proceedings of the 1st ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA),
The generalization of matrixbased Valiant's contextfree language recognition algorithm for graph case is widely considered as a recipe for efficient contextfree path querying; however, no progress has been made in this direction so far. We propose the first generalization of matrixbased Valiant's algorithm for contextfree path querying. Our generalization does not deliver a truly subcubic worstcase complexity algorithm, whose existence still remains a hard open problem in the area. On the other hand, the utilization of matrix operations (such as matrix multiplication) in the process of contextfree path query evaluation makes it possible to efficiently apply a wide class of optimizations and computing techniques, such as GPGPU, parallel processing, sparse matrix representation, distributedmemory computation, etc. Indeed, the evaluation on a set of conventional benchmarks shows, that our algorithm outperforms the existing ones.

arXiv,
Graph data model and graph databases are very popular in various areas such as bioinformatics, semantic web, and social networks. One specific problem in the area is a path querying with constraints formulated in terms of formal grammars. The query in this approach is written as grammar, and paths querying is graph parsing with respect to given grammar. There are several solutions to it, but how to provide structural representation of query result which is practical for answer processing and debugging is still an open problem. In this paper we propose a graph parsing technique which allows one to build such representation with respect to given grammar in polynomial time and space for arbitrary contextfree grammar and graph. Proposed algorithm is based on generalized LL parsing algorithm, while previous solutions are based mostly on CYK or Earley algorithms, which reduces time complexity in some cases.
 Workshop on Multiparadigm Programming with ObjectOriented Languages,

Databased code synthesis in IntelliJ IDEASEIM'18,
 BHV, St.Petersburg,
 In Proceedings of DAMDID / RCDL'2016 (local), pages 132–137, Ershovo,
 Sixteenth International Conference on Machine Learning and Applications (ICMLA),

Dependence of stress granule formation on microtubules supports the idea of stress granule specific “glue” arising in stress conditions54th Annual Meeting of the American Society for Cell Biology,
 PROCEEDING OF THE AINLISMW FRUCT CONFERENCE,

Detecting anomalies in Kotlin code2nd International Workshop on Machine Learning techniques for Programming Languages,