Computer Science Internships
for International Students

About the Faculty

The Faculty of Computer Science has sixteen laboratories engaged in research in theoretical computer science, big data, optimization methods, machine learning, computer vision, software engineering, bioinformatics, and topology. The Faculty cooperates with the most importamt Russian and internations research institutions, including the Russian Academy of Sciences, CERN, Samsung, and Yandex. Scientists from all over the world take part in the work of the laboratories and teaching at the Faculty. Regular conferences, schools, laboratory seminars, and the Colloquium of the Faculty are held.

• TCS Lab - Theoretical Computer Science

  Internship supervisor: Prof. Artem Maksaev

  The Laboratory of Theoretical Computer Science was founded in December 2015. Structurally, it is a part of Big Data and Information Retrieval School at the Faculty of Computer Science.

  The main research directions of the laboratory are computational complexity, algorithmic information theory, algorithmic statistics, combinatorial optimization, algorithmic aspects of game theory.

  Topics: Computational complexity
  Boolean circuit complexity, communication and information complexity
  Algorithmic (Kolmogorov) complexity
  Algorithmic statistics
  Algorithm design and analysis
  Algorithmic game theory
  Combinatorial matrix theory
  Graph theory, Random graphs and hypergraphs
  General algebraic and number theoretic problems

• PAIS Lab - Process-Aware Information Systems

  Internship supervisor: Prof. Irina Lomazova

  Visual Representation of Distributed Systems

  The key research directions in the PAIS Lab are mainly concentrated on formal methods and process mining. Formal methods comprise diverse methods to model and verify the behavior of processes occurring in distributed information systems using, for instance, finite automata, Petri nets and their extensions. Process mining focuses on discovering process models from the sequential records of their execution represented by event logs. Process mining aims to provide the AS-IS view on the processes in contrast to manually created models of idealized processes created at the early stages of the information system life-cycle.

   

  Topics: Development and the experimental evaluation of the new approaches to modeling the behavior of complex distributed information systems using data recorded in event logs

• LAMBDA Lab - Methods for Big Data Analysis

  Internship supervisor: Dr. Denis Derkach

  The main scientific interests of the laboratory: the use and adaptation of machine learning methods to solve problems of natural sciences (and industry).

  By subject areas:

  1. Particle physics - partners JINR (Dubna) and NCFM (Sarov) and other expriments.
  - fast selection of physical information;
  - simulation of the response of the particle flight detector;
  - search for the optimal design of detectors.

  2. Astrophysics 
  - solving the inverse problem.

  3. Material Science 
  - prediction of material properties;
  - simulation of material behavior under external influence.

  4. Atmosphere:

  - short-term weather forecast;
  - weather simulator setup;
  - problem of dispersion from pollution sources.

  From the point of view of machine learning:

  - simulation by generative models - improving the quality of description, estimating the error of description, taking into account external information (laws of conservation or structure of the object);
  - machine learning with structural constraints and information (for example, PINN);
  - estimation of errors in regressions and generative models;
  - optimization in black and gray box problems;
  - acceleration of models;
  - code generation.

  Topics: Use of LLMs for applied tasks (physics/industry)

• MMCP Lab - Models and Methods of Computational Pragmatics 

  Internship supervisor: Dr. Dmitry Ignatov

  Multimodal Clustering for NLP, Knowledge Discovery in Databases and Recommender Systems

  Aims and objectives of the Laboratory
  We research and develop interpretable methods for processing user data, user action histories, and text data. We are creating new methods for processing texts in Russian that take into account such features of the Russian language as a rich morphological system and free word order in a sentence.
  In addition, we consider it important to collect high-quality labeled cases - sources of language data - and publish them in the public domain, as The creation of new sources of language data is an important contribution to the development of the community and the state of the word processing industry as a whole.
  We solve the following tasks:
  Development and comparison of multimodal clustering and classification methods.
  Development and comparison of methods of compression, rarefaction and regularization of neural networks, in particular, recurrent networks for language modeling.
  Marking of cases of question-answer pairs, cases marked up by entities and events and the relationships between them.
  Adaptation of standard architectures for English for the Russian language and development of own solutions for the tasks of finding the answer to a question, semantic parsing, information extraction, detection of quotes and indirect speech, etc.
  Development of models for concise presentation (embeddings) of texts for recommendation systems.
  Methods for extracting semantic patterns from texts based on data mining, analysis of formal concepts and others.
  Understanding the intentions of users of recommendation and other, including mobile services.
  The main activities of the Laboratory
  Scientific and research activities: in accordance with the tasks listed above.
  Educational activities: conducting courses on the subject of the Laboratory at the bachelor's and master's programs of the FCS, open seminars for the general public.
  Project activity: implementation of industrial projects in cooperation with companies.

  Topics: Models and methods for text data analysis, recommender systems and data mining

• CSMC Lab - Complex Systems Modeling and Control

  Internship supervisor: Prof. Viktor Popov

  The laboratory pursues fundamental and applied research on data analysis and mathematical modeling of complex systems. The projects of the Laboratory are focused on the development of mathematical models and numerical methods for the reconstruction of the properties of big systems, which demonstrate synchronization phenomena, quasi-regularities, self-organization, and sudden regime changes. The reconstruction, based on the analysis of non-stationary time series, is performed to predict the dynamics of the underlying systems and create the mechanisms that allows us to control these systems efficiently.

  Topics: Exploring Nonlinear Science in Non-Stationary Time Series and Mathematical Modeling for Complex System Control

• ATG Lab - Algebraic Transformation Groups

  Warning:  Pure Algebra

  Internship supervisor: Prof. Ivan Arzhantsev

  The theory of algebraic transformation groups, i.e., actions of algebraic groups on algebraic varieties,
  is one of the classical areas of algebra and algebraic geometry. It has many interconnections with combinatorics,
  differential geometry, algebraic group theory, Lie groups, Lie algebras, and representation theory.

  One of the main objects of our study is affine algebraic varieties. On the one hand, affine geometry studies local
  properties of arbitrary algebraic varieties; on the other hand, it gives a geometric interpretation of natural questions
  in commutative and differential algebra. The combination of algebraic, geometric, representation-theoretical and
  combinatorial
  methods allows us to use a wide range of tools in modern mathematics and obtain original results.

  The laboratory regularly organizes conferences, schools and seminars in affine algebraic geometry and transformation groups.
  Within the laboratory, students have an opportunity to work on modern research projects, to publish their results in leading
  mathematical journals and to take part in international collaborations.

  Topics: Automorphism groups of algebraic varieties, varieties with torus actions and graded algebras,
  additive actions on complete varieties, locally nilpotent derivations, Cox rings and their applications

• Scientific and Educational Laboratory of Cloud and Mobile Technologies (CMT Lab)

  Internship supervisor: Prof. Dmitry Alexandrov

  The goal of the scientific and educational laboratory is to develop the IT direction related to the development of cloud and
  mobile applications, including highly loaded distributed software, intelligent multi-agent systems and Internet of Things
  systems, as well as systematization and generalization of the accumulated experience of the IT community in the field of
  technologies for developing such systems. The laboratory regularly organizes seminars and summer schools in cloud and mobile development.Within the laboratory, students have an opportunity to work on research and applyed projects, and to publish their results.

  Topics: Research and Development of Methods for Creating Intelligent Multi-agent and IoT Software Systems

• International Laboratory for Intelligent Systems and Structural Analysis

  Internship supervisor: Prof. Vasilii Gromov

  Main tasks of the laboratory

  Fundamental and applied research in the field of data analysis of large volume and complex structure (including complex structured data in textual, numeric, relational and structural forms).
  Development of software tools for data analysis and creating of various components of intelligent systems.
  Integration of the professional community in the field of data analysis  and intelligent systems, facilitating the involvement of novice researchers.
  Involvement of teachers, students and post-graduates of the National Research University Higher School of Economics in scientific work of the laboratory.
  Adopting of research results of the laboratory in the educational process.
  Preparation of new curricula and courses for "Applied Mathematics and Informatics" and "Data Science" majors at the NRU HSE.

  Topics: Building knowledge systems and data analysis based on textual information

• Centre of Deep Learning and Bayesian Methods

  Internship supervisor: Prof. Aibek Alanov

  The centre conducts research at the intersection of two actively developing areas of data analysis: deep learning and Bayesian methods of machine learning methods. Deep learning is a section that involves building very complex models (neural networks) to solve problems such as classifying images or music, transferring an art style from picture to photograph, predicting the next words in a text. Within the framework of the Bayesian approach, probabilistic models based on the apparatus of probability theory and mathematical statistics are considered for solving such problems.

  Topics: Efficient parameterizations for generative models, including GANs and diffusion models; Novel approaches to Bayesian deep learning and uncertainty quantification;     Speech enhancement and neural vocoding (e.g.,HiFi++, FFC-SE); Development of controllable generative AI systems; Research on acceleration methods for diffusion models

About HSE University

Consistently ranked as one of Russia’s top universities, HSE University is a leader in Russian education and one of the preeminent universities in eastern Europe and Eurasia. Having rapidly grown into a well-renowned research university over two decades, HSE University sets itself apart with its international presence and cooperation.

In March 2014 HSE University together with Yandex, a major Russian IT company, opened its new Faculty of Computer Science. The Faculty aims at preparing highly qualified data scientists, software engineers, and computer science researchers for leading Russian and international IT companies and academic institutions.

Past participants

Students from Oxford University (UK), École Normale Supérieure de Paris (France), Università degli Studi di Padova (Italy), Université de Toulouse (France), Instituto Superior Técnico (Portugal), École Centrale Supérieure de Marseille (France), INSA Lyon (France) have participated in our internships.