The Faculty of Computer Science invites international students to participate in research internships. The duration of the internship is two to six months.
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.
Requirements
- Being an undergraduate, graduate, or postgraduate student
- Experience in the research area of the chosen internship
- Good command of English (Russian is not required)
Application procedure
Your application should include:
- CV
- Cover letter
- University transcript with your grades
- The name of the project you’re interested in
- English proficiency certificate (not obligatory)
On successful completion of the initial selection, the candidate is invited for an interview with a potential academic supervisor. The interview is conducted in English.
Important notice
Please note that the review process may take a few weeks. We will get back to you as soon as we have made our decision, whether positive or negative. No application will remain unanswered!
You can apply for an online or offline internship or consult us.
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TCS Lab - Theoretical Computer Science
Internship supervisor: Prof. Vladimir Gurvich, Prof. Michael Vyalyi
Solving combinatorial games
Studying Sprague-Grundy and and Smith functions of combinatorial games hypergraph NIM and slow NIM. Constructing efficient algorithms solving these games.
Prerequisites: Good skills in combinatorial proofs, a rather good experience in solving combinatorial problems by computations
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PAIS Lab - Process-Aware Information Systems
Internship supervisor: Dr. Alexey Mitsyuk
Visual Representation of Distributed Systems
In this project, your goal will be to prepare a research report with comparative analysis of existing methods to visualize distributed systems. Both structural (static) and behavioral (dynamic) aspects are important. Good reading and writing skills are needed. Besides, it is expected that you not just find and mention but try to reproduce considered methods of visual representation. Well-made project report can be a starting point for further research collaboration, for example, on the base of HSE University PhD School.
2D Graph Drawing Methods
In this project, your goal will be to study and survey various graph drawing methods. We are interested more in so called layered layouts (Sugiyama-style algorithms etc.), but other human-readable layouts can be considered as well. You will have to define some comparison criteria (including theoretical and practical algorithmic complexity) and compare graph layout algorithms according to them. So, a good knowledge of algorithms is needed. The output of this project will be a research report that can lay a foundation for further research collaboration, for example, on the base of HSE University PhD School.
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MTMML Lab - Matrix and Tensor Methods in Machine Learning
Internship supervisor: Dr. Maxim Rakhuba
Tensor decompositions and applications
Tensor decompositions naturally generalize the concept of singular value decompostion (SVD) to the case of multidimensional arrays (tensors). In this project you will work on developing tensor approximation algorithms based on modern techniques such as Riemannian optimization or randomized algorithms. Possible application of the algorithms include compression of neural networks and building recommender systems.
Improving robustness of neural networks
Large neural networks can often be affected by instabilities of training or be sensitive to specifically constructed perturbations of data (adversarial attacks). It appears that the arsing instabilities heavily depend on spectral properties of neural network building blocks. The project will be devoted to developing algorithms for improving these spectral properties using numerical linear algebra techniques.
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LAMBDA Lab - Methods for Big Data Analysis
Internship supervisor: Dr. Denis Derkach
NL2ML (Natural language to machine learning)
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MMCP Lab - Models and Methods of Computational Pragmatics
Internship supervisor: Dr. Dmitry Ignatov
Multimodal Clustering for NLP, Knowledge Discovery in Databases and Recommender Systems
Machine Learning on Graphs (including graph embeddings)
Benchmarking Deep Learning and Neural Architecture Search for NLP
Sparsification and Compression of Deep Learning models
Interpretable and Fair Machine Learning and Recommender Systems
Combinatorial Search and Optimization with Formal Concept Analysis
Boolean Matrix Factorization and its Applications (including RecSys)
Sequence, Stream and Graph Mining and their applications
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Internship supervisor: Dr. Muhammad Shahid Iqbal Malik
Cross-lingual and Multi-lingual Text Classification and its Explainability for Abusive Content Detection from Social Media
Neural NLP learning: Exploration of multi-task learning, transfer learning, and active learning approaches for Social Media Mining
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CSMC Lab - Complex Systems Modeling and Control
Internship supervisor: Prof. Viktor Popov
Data analysis and modelling of processes in the Sun (in the stars), in the solar (stellar) wind, in the magnetospheres of planets (exoplanets)
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ATA Lab - Algebraic Topology and Its Applications
Internship supervisor: Dr. Yakov Veryovkin
Theory of n-valued groups, hyperbolic geometry and applications
Theory of branched coverings
Group actions on manifolds, algebraic topology and combinatorics of orbit spaces
Bigraded persistent double homology and torus actions
Hyperbolic manifolds, reflection groups and cohomological rigidity
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ATG Lab - Algebraic Transformation Groups
Warning: Pure Algebra
Internship supervisor: Prof. Ivan Arzhantsev
Locally nilpotent derivations and automorphisms of algebraic varieties
We study automorphism groups of both affine and complete complex algebraic varieties applying algebraic and combinatorial methods. Main tools include graded algebras, locally nilpotent derivations, Cox rings and Demazure roots of toric varieties.
Prerequisites: Basic knowledge of Commutative Algebra, Algebraic Geometry and Representation Theory
Process Simulation and Log Generation
Gena is a simple tool that allows generating event logs by executing process models. It is useful in the testing and evaluation of process mining algorithms. Gena supports two important notations used in business process modelling and process mining: Petri nets and basic BPMN models. However, many improvements can be made in Gena to support more sophisticated simulation scenarios. Interns have an opportunity to join this project.
Closures and lattices for data analysis and knowledge discovery
We study applications of interpretable (explainable) tools of lattice and order theory, including closed descriptions, minimal generators (keys) in projects on data analysis and knowledge discovery.
Geometry of J
The first goal of this project is to further understand the geometric properties of this metric. For instance, one objective would be to study whether the set P(R^d), endowed with J, is a geodesic metric space, characterize its shortest paths and eventually its curvature properties. Detailed description.
Barycenters
A related field of investigation is to study barycenters relative to metric J. One natural question is whether such barycenters exist, are unique and if one can estimate them in a consistent way. These questions could be studied in the light of the recent paper by Le Gouic and Loubes (2017). Detailed description.
Variance inequalities
Given the recent work by Ahidar-Coutrix et al. (2018), another interesting question is whether one can establish so-called variance inequalities for the metric J. Such inequality was shown to guarantee fast convergence rates for certain estimators of barycenters in Ahidar-Coutrix et al. (2018) and would be of great interest from a statistical point of view. Detailed description.
Data analysis and modelling of processes in the Sun (in the stars), in the solar (stellar) wind, in the magnetospheres of planets (exoplanets)
Data analysis, monitoring, modelling and forecasting of space weather.
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.
Frequently asked questions
We invite current undergraduate, graduate, and postgraduate students from all over the world. The key requirement is experience in the research area of the internship.
No, you do not need provide such certificate. However, your interview with a potential academic supervisor will be conducted in English.
No, there is not any, all internships are offered free of charge.
You can apply for financial aid from your university.
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.
Cecilia Tosciry (Oxford University)
In Moscow, I worked with two researchers: Professor Fеdor Ratnikov of HSE University, who works on the LHCb experiment at CERN, and Andrey Ustyuzhanin, who is responsible for joint projects of CERN and Yandex. At the beginning of the internship, Andrey Ustyuzhanin and I discussed my project in detail: he asked me about research problems and advised me on relevant articles. This was very useful: I learned about various algorithms for finding similarities between objects. But the trip to Moscow was remembered for more than just research. I was delighted with the local food - it was like going on holiday to a hospitable grandmother's house. All in all, the trip went well, and I even learned a little Russian.
Belhal Karimi (Ecole polytechnique (Université Paris-Saclay)
I really liked the organisation of the research, students and supervisors working on projects together. There were six or ten researchers in the lab every day, and we helped each other informally, sharing the results of experiments. Perhaps I will come to Moscow again: my supervisor at the Polytechnic School often goes to Russia. I enjoyed this trip - especially the Mayakovskaya district, where I lived, and the weekend I spent in St. Petersburg.
Leo Botelle (École Normale Supérieure de Paris)
I wanted to go to Russia for a long time, so I started reading about universities with strong data analysis programmes in Moscow. HSE University turned out to be one of them. At first, when I first arrived there, I was going to research applications of machine learning to build social graphs. The new theme was suggested by Sergey [Kuznetsov]: it turned out to be quite complicated and required a strong mathematical background. However, during the two months I spent in Moscow, I was able to sharpen my skills, which will be useful in my subsequent research.
Diego Granziol (Oxford University)
I cannot stress enough how lucky I was and what an honour it was to come to Russia, to work with the whole Bayesian Methods Research Group. The atmosphere was warm and welcoming. Timur [Garilov] is a truly amazing coder, and without him I would not have moved forward on any of my own ideas. I think he has the potential to do truly quite exceptional research, and I'm incredibly happy to follow his progress. I thank Dmitry [Vetrov], whose final contribution to the paper we sent to NeurIPS was absolutely essential, for our regular meetings, advice, questions, support and time.
Contacts
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