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Maksim Eremeev

Senior BSc. student of Lomonosov Moscow State University, faculty of Computational Mathematics and Cybernetics, Mathematical Methods of Forecasting department.

Scientific Advisor: Konstantin Vorontsov

SCOPUS ID = 57212339802

ORCID = 0000-0001-7459-7290

My LinkedIn

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Research Interests

Majoring in Machine Learning and Data Analysis, my primary research interests are Natural Language Processing, Topic Modeling and Information Search.

Currently, I conduct research on automatic estimation of text complexity and generating reading orders. If succeeded, the novel algorithms in ranking exploratory search results will be introduced.

Conference Talks

Date Event Topic Materials
20.02.2020 Poster Session at OpenTalks.AI conference Automatically estimating text complexity Poster
29.11.2019 Talk at Mathematical Methods of Pattern Recognition conference (MMPR) Quantile-based approach to estimating cognitive complexity Slides
04.09.2019 Poster Session at Recent Advances of Natural Language Processing conference (RANLP) Lexical Quantile-based Text Complexity Measure Poster
11.05.2019 Talk at ODS DataFest conference, Science Day Estimating text complexity to rank exploratory search results Slides


10.04.2019 National Students conference Lomonosov, Mathematical Methods of Forecasting section Exploratory search based on Topic Modeling Slides


  1. M.Eremeev, A.Yanina. 2019. Exploratory Search based on Topic Modelling (in Russian). Book of Abstracts of XXVI International Conference of Students, Graduates and Young Scientists “Lomonosov-2019”, Computational Mathematics and Cybernetics section, Moscow, Russia, 2019. Text
  2. M.Eremeev, K.Vorontsov. 2019. Lexical Quantile-Based Text Complexity Measure. In Proceedings of the 12th International Conference on “Recent Advances in Natural Language Processing” (Scopus-indexed), Varna, Bulgaria, 2019. Text
  3. M.Eremeev, K.Vorontsov. 2019. Quantile-based approach of measuring cognitive complexity of text. In proceedings of Russian National Conference MMPR-2019, Moscow, Russia, 2019. Text
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