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Regular version of the site
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Article
Mint: MDL-based approach for Mining INTeresting Numerical Pattern Sets

Makhalova T., Kuznetsov S., Napoli A.

Data Mining and Knowledge Discovery. 2022. P. 108-145.

Article
Human knowledge models: Learning applied knowledge from the data

Dudyrev E., Semenkov Ilia, Kuznetsov S. et al.

Plos One. 2022. Vol. 17. No. 10.

Book chapter
Modeling Generalization in Domain Taxonomies Using a Maximum Likelihood Criterion

Ajrapetyan Z., Mirkin B., Trevor F. et al.

In bk.: WorldCIST 2022.Information Systems and Technologies.Lecture Notes in Networks and Systems. Iss. 469. Springer, 2022. P. 141-147.

Book chapter
Ontology-Controlled Automated Cumulative Scaffolding for Personalized Adaptive Learning

Dudyrev F., Neznanov A., Anisimova K.

In bk.: Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium -23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, Proceedings, Part II. Springer, 2022. P. 436-439.

Book chapter
Triclustering in Big Data Setting

Egurnov D., Точилкин Д. С., Ignatov D. I.

In bk.: Complex Data Analytics with Formal Concept Analysis. Springer, 2022. P. 239-258.

Article
Triclusters of Close Values for the Analysis of 3D Data

Egurnov D., Ignatov D. I.

Automation and Remote Control. 2022. Vol. 83. No. 6. P. 894-902.

Article
Deep Convolutional Neural Networks Help Scoring Tandem Mass Spectrometry Data in Database-Searching Approaches

Kudriavtseva P., Kashkinov M., Kertész-Farkas A.

Journal of Proteome Research. 2021. Vol. 20. No. 10. P. 4708-4717.

Article
Language models for some extensions of the Lambek calculus

Kanovich M., Kuznetsov S., Scedrov A.

Information and Computation. 2022. Vol. 287.

Graduate of the School of Data Analysis and Artificial Intelligence Defends Thesis at TU Dresden

On August 21 Artem Revenko defended his thesis on ‘Automatic Construction of Implicative Theories for Mathematical Domains’ at the Faculty of Computer Science at Dresden University of Technology.

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.