- 2006 – 2012 Saint Petersburg State University, master's degree in Information Technology, thesis: Automated transformation of dynamic SQL queries in information system reengineering.
- 2012 – 2016 Ph.D. student at Saint Petersburg State University. PhD thesis: Parsing of dynamically generated code.
- 2013 – 2017 Senior Lecturer at the Saint Petersburg State University.
- 2012 – to date researcher at JetBrains.
- 2017 – to date Associate Professor at the Saint Petersburg State University.
Formal language theory and application for biology, graph databases, static code analysis, formal grammars and other languages specification formalisms, parallel and asynchronous computations.
- Formal Language Theory
- Graph Theory
- Algorithms and Data Structures
- Practice of Programming
- Russian Science Foundation grant 18-11-00100 (2018-today)
- Russian Foundation for Basic Research grant 18-01-00380 А (2018-today)
- Russian Foundation for Basic Research grant 15-01-05431 А (2015-2017)
- SEIM-2017 -2018 -2019: PC member
- CIBB-2019 (Algebraic and Computational Methods for the Study of RNA Behaviour): PC member
- Invited research speaker at Inria-LINKS
.NET and GPGPU integration based on F# quotations to OpenCL translator.
Modular tool for parser construction and grammars processing
The composition of formal grammars and artificial neural networks for secondary structure analysis.
Logic, Language, Information, and Computation, June 2019
The Bar-Hillel theorem states that context-free languages are closed under intersection with a regular set. This theorem has a constructive proof and thus provides a formal justification of correctness of the algorithms for applications mentioned above. Mechanization of the Bar-Hillel theorem, therefore, is both a fundamental result of formal language theory and a basis for the certified implementation of the algorithms for applications. In this work, we present the mechanized proof of the Bar-Hillel theorem in Coq.
Recently proposed matrix multiplication based algorithm for context-free path querying (CFPQ) offloads the most performance-critical parts onto boolean matrices multiplication. Thus, it is possible to achieve high performance of CFPQ by means of modern parallel hardware and software. In this paper, we provide results of empirical performance comparison of different implementations of this algorithm on both real-world data and synthetic data for the worst cases.Proceedings of the 2nd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA), June 2019
Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOINFORMATICS, March 2019
We propose a way to combine formal grammars and artificial neural networks for biological sequences processing. Formal grammars encode the secondary structure of the sequence and neural networks deal with mutations and noise. In contrast to the classical way, when probabilistic grammars are used for secondary structure modeling, we propose to use arbitrary (not probabilistic) grammars which simplifies grammar creation. Instead of modeling the structure of the whole sequence, we create a grammar which only describes features of the secondary structure. Then we use matrix-based parsing to extract features: the fact that some substring can be derived from some nonterminal is a feature. After that, we use a dense neural network to process features.
Extended abstract at TyDe 2018 (at ICFP).
Proceedings of the 9th ACM SIGPLAN International Symposium on Scala, September 2018
Transparent integration of a domain-specific language for specification of context-free path queries (CFPQs) into a general-purpose programming language as well as static checking of errors in queries may greatly simplify the development of applications using CFPQs. LINQ and ORM can be used for the integration, but they have issues with flexibility: query decomposition and reusing of subqueries are a challenge. Adaptation of parser combinators technique for paths querying may solve these problems. Conventional parser combinators process linear input, and only the Trails library is known to apply this technique for path querying. We demonstrate that it is possible to create general parser combinators for CFPQ which support arbitrary context-free grammars and arbitrary input graphs. We implement a library of such parser combinators and show that it is applicable for realistic tasks.
Proceedings of the 13th Central & Eastern European Software Engineering Conference in Russia (CEE-SECR '17), December 2017
There are several solutions for CFPQ, 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 context-free 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.
Perspectives of System Informatics, June 2016
We present a technique for syntax analysis of a regular set of input strings. This problem is relevant for the analysis of string-embedded languages when a host program generates clauses of embedded language at run time. Our technique is based on a generalization of RNGLR algorithm, which, inherently, allows us to construct a finite representation of parse forest for regularly approximated set of input strings. This representation can be further utilized for semantic analysis and transformations in the context of reengineering, code maintenance, program understanding etc. The approach in question implements relaxed parsing: non-recognized strings in approximation set are ignored with no error detection.
Programming Languages and Tools Lab Head of group, researcher
- Formal Grammars and Languages
- Syntax Analysis
- Parsing Algorithms