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September 25, 2026
AI Users Earn Up to 41.8% More Than Non-Users
Research conducted by economists at HSE University has revealed a significant correlation between the regular use of GenAI in the workplace and higher pay among Russian employees. The study found that individuals who frequently use GenAI in their professional activities earn notably more than those who reject these new tools or resort to them occasionally. The salary premium for highly qualified specialists reaches 41.8%. The article was published in the Voprosy Ekonomiki journal.
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Vincent Fardeau, Associate Professor at HSE ICEF, has reached a major career milestone: he recently published his paper ‘Asymmetric Thin Markets’ in the Journal of Financial Economics, successfully passed his major academic review, and received tenure. In this interview, Vincent discusses the story behind the paper, explains the concept of asymmetric thin markets, and shares his advice for young scholars aiming to publish in top-tier journals.
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Personal Interest in Doctoral Thesis Topic Most Important for Confidence in Successful Defence
A researcher at HSE University analysed data on 1,539 doctoral students from 161 Russian universities to identify which features of a thesis topic are associated with academic success and engagement. The most important factor was found to be personal interest in the research topic, which was associated with almost all key aspects of doctoral programme experience—from engaging with the academic supervisor to research activity and confidence about successfully defending the thesis. The findings have been published in Higher Education.

 

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Ontology and DSL co-evolution using graph transformations methods

P. 233–247.
Boris Ulitin, Eduard Babkin

The article is related to the problem of implementing consistent changes in the model of some subject area and in the syntax of domain specific language (DSL), dealing with problems in that domain. In our research, we explore an opportunity to provide the method of co-evolution of the ontology, used as a model of the subject area, and DSL. This method combines graph representation of the ontology and DSL with the set of rules, formulated in terms of an automated graph-transformation language. Applicability of the proposed approach is demonstrated using a real-life example of co-evolution of the ontology and DSL in the railway transportation domain.

Language: English
DOI
Keywords: ontologyevolutiondomain-specific languagegraph transformationco-evolutionrailway transportation

In book

Lecture Notes in Business Information Processing
Issue 295: Perspectives in Business Informatics Research. , Switzerland: Springer, 2017.
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