Evgenii Vityaev

Novosibirsk State University, Sobolev Institute of Mathematics

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About

Evgenii Vityaev is a pioneering researcher in symbolic and relational data mining, with a particular focus on bridging the gap between logical methods and numeric data analysis. His major contributions center on developing symbolic methodologies for data mining that offer an alternative to dominant statistical and neural network approaches, proving especially effective in robotics, drug design, and financial applications. His 2008 paper "Symbolic methodology for numeric data mining" (8 citations) and 2002 work on relational techniques for financial applications (6 citations) established foundational frameworks for applying logical-probabilistic methods to complex, real-world datasets. Vityaev has also made significant strides in predictive modeling, as demonstrated in his 2009 paper "New definition of prediction without logical inference" (7 citations), which addresses fundamental challenges in AI prediction tasks for expert systems, decision support, and robotics. His adaptive control research on modular and multiped robots (2017-2018) showcases practical applications of his logical-probabilistic algorithms, including directed search of rules and joint training of control modules. Vityaev's work uniquely combines symbolic reasoning with probabilistic learning, offering powerful tools for domains where traditional black-box methods fall short.