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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.
September 24, 2026
‘Feedback and Constructive Criticism Are Essential in Our Profession
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.
September 22, 2026
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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Soundness Correction of Data Petri Nets

IEEE Access. 2025. Vol. 13. P. 149142–149157.
Suvorov N. M., Lomazova I. A.

A process model is called sound if it always terminates properly and each model activity can occur in a process instance. Conducting soundness verification right after process design enables the detection and elimination of design errors in a process to be implemented. The process of eliminating such errors is called soundness repair. In many repair scenarios, the resulting model should retain only the correct behavior of the source model, especially if a model is created manually. In this paper, we consider this type of soundness repair applied to data-aware process models represented as data Petri nets (DPNs). We investigate the capabilities to repair soundness of DPNs by restricting the transition guards and disprove some earlier statements regarding it. We propose a new repair algorithm that follows this approach, with a key distinction that it does not require an input DPN to have a sound control flow. The algorithm is implemented, and the results of its preliminary evaluation justify its practical applicability in realistic application domains.

Research target: Computer Science
Language: English
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Keywords: бездефектностьИсправление моделей процессовData Petri netsData-aware soundnessdata-aware processessoundness repairсеть Петри с даннымимодели процессов с данными
Publication based on the results of:
New methods in the study of formal computation models, computer systems and related theoretical computer science problems (2025)
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