We use cookies in order to improve the quality and usability of the HSE website. More information about the use of cookies is available here, and the regulations on processing personal data can be found here. By continuing to use the site, you hereby confirm that you have been informed of the use of cookies by the HSE website and agree with our rules for processing personal data. You may disable cookies in your browser settings.
✖Pokrovsky boulevard, 11, room S938, Moscow, Russia, 109028
Phone: +7 (495) 772-95-90*27319
The School of Data Analysis and Artificial Intelligence was created in 2014 as part of the Department of Data Analysis and Artificial Intelligence. The school consists of world-renowned researchers who actively participate in international research projects in the fields of data mining, formal concept analysis, semantic technologies and ontology engineering, multi-modal clustering, machine learning, natural language processing, development of intelligent and recommender systems, social network analysis, and medical informatics.
Artem Revenko graduated from the HSE’s master’s programme in mathematical modelling in 2011 and doctoral programme in 2013.
Sergei Kuznetsov, Head of the School of School of Data Analysis and Artificial Intelligence was Artem’s Academic Supervisor during his master’s and doctoral studies. While studying at HSE, Artem also studied at TU Dresden, and hence the latest PhD thesis he defended in Dresden was based on his Moscow work.
The idea offered by Artem is based on Formal Concept Analysis and helps automatically generate formal mathematical theories. Put simply, the software system developed by Artem on the basis of this approach helps to find evidence of two mathematical results from general algebra. A researcher, working manually, might spend up to 3 years one of these results and 10 years on the other. The thesis committee included two Academic Supervisors, Bernhard Ganter and Gernot Salzer, two experts in computer science and a specialist in the thesis area. Artem’s work was graded as ‘Magna cum laude’, corresponding to a score of 8 points on the ten-point grading scale.