The main focus of our group is Natural Language Processing (NLP) applied for various tasks and settings with a special emphasis on semantic processing. We teach how to build models that extract meaning from text. Students usually work in groups to create interesting experiments, demos, and applications, that use methods of machine learning, deep learning, to solve various NLP tasks. We try to design each project so that it has the potential to mature into a real application or experiment which an be presented at an international scientific conference, such as ACL, EMNLP, LREC, or the associated workshops.


  • Statistical Natural Language Processing (Spring, 2019). A course for Master students. Slides. The course is based on the classic textbook of Jurafsky & Martin and represents a set of topics on (mostly) pre-neural NLP.
  • Neural Natural Language Processing (Winter, 2019). A course for Master students. Slides. This course is focusing mostly on neural NLP models.

Master Theses

We offer our students interesting topics that allow them to work on our projects and doing real scientific work. This involves close collaboration with the research scientists and Ph.D. students of our group, but also eventually collaborators in the University of Hamburg, University of Halle, and some other our close collaborators so the students get sufficient support.

Currently enrolled master students:

  • Anton Razzhigaev (Skoltech)
  • Anton Voronov (Skoltech)
  • Igor Markov (Skoltech)
  • Dmitry Puzyrev (Skoltech-HSE)
  • Lyubov Kupriyanova (Skoltech)


  • Dr. Artem Shelmanov (Research Scientist), now with Sberbank AI
  • Denis Teslenko (Master student, Ural Federal University), now with Sberbank
  • Filip Furaev (Master student, Skoltech-GUAP)
  • Nikita Borovkov (Master student, Skoltech-GUAP)