Leonid E Zhukov
- Leonid E Zhukov has been at HSE since 2008.
Education and Degrees
- 1998PhD
- 1998
Doctor of Sciences*
- 1993
Degree
Moscow State Engineering and Physics Institute
* Doctor of Sciences
A post-doctoral degree called Doctor of Sciences is given to reflect second advanced research qualifications or higher doctorates in ISCED 2011.
A post-doctoral degree called Doctor of Sciences is given to reflect second advanced research qualifications or higher doctorates in ISCED 2011.
Student Term / Thesis Papers
- Master
A. Kurmukov «Classification of Normal and Pathological Brain Networks Based on Similarity of Graph Partitions». Faculty of Computer Science, 2017
P. Sulimov «Link Prediction and Contagion Forecasting in the Economic Network». Faculty of Economic Sciences, 2016
S. Korolev «Machine Learning Methods for Particle Identification in Data of the LHCb (CERN) Detector». Faculty of Computer Science, 2016
Y. Dodonova «Classification between Norm and Pathology Based on Spectral Features of Network Brain Structures». Faculty of Computer Science, 2016
D. Petrov «Advanced feature engineering and dimensionality reduction for structural connectome classification». Faculty of Computer Science, 2016
Courses (2018/2019)
- Network Science (Master’s programme; Faculty of Computer Science; programme "Data Science"; 1 year, 3, 4 module)Eng
- Social Network Analysis (Master’s programme; 1 year, 4 module)Eng
- Social Networks (Master’s programme; Faculty of Humanities; programme "Computational Linguistics"; 2 year, 1, 2 module)Eng
- Past Courses
Courses (2017/2018)
- Network Science (Master’s programme; Faculty of Computer Science; programme "Data Science"; 1 year, 3, 4 module)Eng
- Scientific Seminar ''Intelligent Systems and Structural Analysis'' (Master’s programme; Faculty of Computer Science; programme "Data Science"; 2 year, 1-3 module)Eng
- Social Network Analysis (Master’s programme; 1 year, 4 module)Eng
- Social Networks (Master’s programme; Faculty of Humanities; programme "Computational Linguistics"; 2 year, 1, 2 module)Eng
Courses (2016/2017)
- Network Science (Master’s programme; Faculty of Computer Science; 1 year, 3, 4 module)Eng
- Social Networks (Master’s programme; Faculty of Humanities; 2 year, 1, 2 module)Eng
- Stochastic Modellling (Master’s programme; Faculty of Computer Science; 2 year, 1, 2 module)Eng
Courses (2015/2016)
- Introduction to Data Science (Optional course (faculty); Faculty of Computer Science; 1-4 module)Eng
- Network Science (Master’s programme; Faculty of Computer Science; 1 year, 3, 4 module)Rus
- Social Network Analysis (Mago-Lego; 4 module)Eng
- Social Networks (Master’s programme; Faculty of Humanities; 2 year, 1, 2 module)Eng
- Stochastic Modellling (Master’s programme; Faculty of Computer Science; spec. "Интеллектуальные системы и структурный анализ "; 2 year, 1, 2 module)Eng
Courses (2014/2015)
- Scientific Seminar ''Intelligent Systems and Structural Analysis'' (Master’s programme; Faculty of Computer Science; spec. "Интеллектуальные системы и структурный анализ "; 1 year, 2-4 module)Rus
- Social Networks (Master’s programme; Faculty of Humanities; 2 year, 1, 2 module)Rus
Stochastic Modellling (Master’s programme; Faculty of Computer Science; "Анализ интернет-данных", "Интеллектуальные системы"; 2 year, 1, 2 module)Rus
- Structural analysis and visualization of networks (Master’s programme; Faculty of Computer Science; spec. "Интеллектуальные системы и структурный анализ "; 1 year, 3, 4 module)Eng
Courses (2013/2014)
- Scientific Seminar "Intelligent Systems" (Master’s programme; School of Applied Mathematics and Information Science; spec. "Интеллектуальные системы"; 2 year, 1-4 module)Rus
- Scientific Seminar "Intelligent Systems" (Master’s programme; School of Applied Mathematics and Information Science; spec. "Интеллектуальные системы"; 1 year, 2-4 module)Rus
- Social Networks (Master’s programme; Faculty of Philology; 2 year, 1, 2 module)Rus
- Social Network Theory (Master’s programme; School of Applied Mathematics and Information Science; spec. "Интеллектуальные системы"; 1 year, 3, 4 module)Rus
Stochastic Modellling (Master’s programme; School of Applied Mathematics and Information Science; "Интеллектуальные системы", "Анализ интернет-данных"; 2 year, 1, 2 module)Rus
Courses (2012/2013)
- Scientific Seminar "Intelligent Systems" (Master’s programme; School of Applied Mathematics and Information Science; spec. "Интеллектуальные системы"; 2 year, 1-4 module)Rus
- Scientific Seminar "Intelligent Systems" (Master’s programme; School of Applied Mathematics and Information Science; spec. "Интеллектуальные системы"; 1 year, 2-4 module)Rus
- Social Networks (Master’s programme; Faculty of Philology; 1 year, 3 module)Rus
- Social Network Theory (Master’s programme; School of Applied Mathematics and Information Science; spec. "Интеллектуальные системы"; 1 year, 3, 4 module)Rus
- Stochastic Modellling (Master’s programme; School of Applied Mathematics and Information Science; spec. "Интеллектуальные системы"; 2 year, 1, 2 module)Rus
Courses (2011/2012)
- Scientific Seminar "Intelligent Systems" (Master’s programme; School of Applied Mathematics and Information Science; spec. "Интеллектуальные системы"; 1 year, 2-4 module)Rus
- Social Network Theory (Master’s programme; School of Applied Mathematics and Information Science; spec. "Интеллектуальные системы"; 1 year, 3, 4 module)Rus
- Stochastic Modellling (Master’s programme; School of Applied Mathematics and Information Science; 2 year, 1, 2 module)Rus
Courses (2010/2011)
- Modern Computer Technologies for Mathematical Modeling (Master’s programme; School of Applied Mathematics and Information Science; 1 year, 1, 2 module)Rus
- Physics (Bachelor’s programme; School of Applied Mathematics and Information Science; 2 year, 3, 4 module)Rus
- Stochastic Modellling (Master’s programme; School of Applied Mathematics and Information Science; 2 year, 1, 2 module)Rus
Conferences
- 2016
The 6th International Conference on Network Analysis (Nizhny Novgorod). Presentation: Co-author Recommender System
- 2014Большие Данные в национальной экономике (Москва). Presentation: Профессия Data Scientist
- 2014 IEEE International Conference on Data Mining, ICDM 2014 (Shenzhen). Presentation: Parallel Corpus Approach for Name Matching in Record Linkage.
Publications26
- Chapter Makarov I., Gerasimova O., Sulimov P., Zhukov L. E. Application of Graph Embedding to Constructing Graph-based Recommender System, in: Proceedings of WebSci’18 Main Conference Poster Session. Aachen : CEUR Workshop Proceedings, 2018. Ch. 1. P. 1-2. (in press)
- Chapter Makarov I., Gerasimova O., Sulimov P., Zhukov L. E. Co-authorship Network Embedding and Recommending Collaborators via Network Embedding, in: Proceedings of Analysis of Images, Social Networks and Texts – 7th International Conference, AIST 2018, Moscow, Russia, July 5-7, 2018, Revised Selected Papers. Lecture Notes in Computer Science / Ed. by W. van der Aalst,, V. Batagelj, G. Glavaš,, D. I. Ignatov, M. Khachay,, O. Koltsova,, S. Kuznetsov, I. A. Lomazova, N. Loukachevitch,, A. Napoli,, A. Savchenko, A. Panchenko,, P. M. Pardalos, M. Pelillo,. Berlin : Springer, 2018. P. 1-6. (in press)
- Chapter Kostyakova Nadezhda, Karpov I., Makarov I., Zhukov L. E. Commercial Astroturfing Detection in Social Networks, in: Computational Aspects and Applications in Large-Scale Networks. Springer Proceedings in Mathematics & Statistics Vol. 247. Springer International Publishing AG, 2018. doi P. 309-318. doi
- Chapter Laptsuev Rodion, Ananyeva Marina, Meinster Dmitry, Karpov I., Makarov I., Zhukov L. E. Information Propagation Strategies in Online Social Networks, in: Computational Aspects and Applications in Large-Scale Networks. Springer Proceedings in Mathematics & Statistics Vol. 247. Springer International Publishing AG, 2018. doi P. 319-328. doi
- Chapter Makarov I., Gerasimova O., Sulimov P., Ksenia Korovina, Zhukov L. E. Joint Node-Edge Network Embedding for Link Prediction, in: Proceedings of Analysis of Images, Social Networks and Texts – 7th International Conference, AIST 2018, Moscow, Russia, July 5-7, 2018, Revised Selected Papers. Lecture Notes in Computer Science / Ed. by W. van der Aalst,, V. Batagelj, G. Glavaš,, D. I. Ignatov, M. Khachay,, O. Koltsova,, S. Kuznetsov, I. A. Lomazova, N. Loukachevitch,, A. Napoli,, A. Savchenko, A. Panchenko,, P. M. Pardalos, M. Pelillo,. Berlin : Springer, 2018. P. 1-12. (in press)
- Chapter Makarov I., Gerasimova O., Sulimov P., Zhukov L. E. Recommending Co-authorship via Network Embeddings and Feature Engineering: The case of National Research University Higher School of Economics, in: Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries. NY : Association for Computing Machinery (ACM), 2018. P. 365-366. doi
- Chapter Makarov I., Bulanov O., Olga Gerasimova, Natalia Meshcheryakova, Karpov I., Zhukov L. E. Scientific Matchmaker: Collaborator Recommender System, in: Analysis of Images, Social Networks and Texts. 6th International Conference, 2017, Lecture Notes in Computer Science, Revised Selected Papers / Ed. by W. M. van der Aalst, D. I. Ignatov, M. Khachay, S. Kuznetsov, V. Lempitsky, I. A. Lomazova, A. Napoli, A. Panchenko, P. M. Pardalos, A. V. Savchenko, S. Wasserman. Vol. 10716. Cham : Springer, 2018. doi P. 404-410. doi
- Chapter Kurmukov A., Dodonova Y., Burova M., Mussabayeva A., Petrov D., Faskowitz J., Zhukov L. E. Topological modules of human brain networks are anatomically embedded: evidence from modularity analysis at multiple scales, in: Computational Aspects and Applications in Large-Scale Networks. Springer Proceedings in Mathematics & Statistics Vol. 247. Springer International Publishing AG, 2018. doi P. 299-308. doi
- Chapter Makarov I., Bulanov O., Zhukov L. E. Co-author Recommender System, in: Models, Algorithms, and Technologies for Network Analysis. Springer Proceedings in Mathematics & Statistics / Ed. by V. A. Kalyagin, A. I. Nikolaev, P. M. Pardalos, O. Prokopyev . Vol. 197. Springer, 2017. doi P. 251-257. doi
- Chapter Kurmukov A., Dodonova Y., Zhukov L. E. Machine learning application to human brain network studies: a kernel approach, in: Models, Algorithms, and Technologies for Network Analysis. Springer Proceedings in Mathematics & Statistics / Ed. by V. A. Kalyagin, A. I. Nikolaev, P. M. Pardalos, O. Prokopyev . Vol. 197. Springer, 2017. doi (in press)
- Chapter Rustem M. Khayrullin, Makarov I., Zhukov L. E. Predicting Psychology Attributes of a Social Network User, in: Proceedings of the Fourth Workshop on Experimental Economics and Machine Learning (EEML 2017), Dresden, Germany, September 17-18, 2017 / Ed. by R. Tagiew, D. I. Ignatov, A. Hilbert, K. Heinrich, R. Delhibabu. Vol. 1968. Aachen : CEUR Workshop Proceedings, 2017. P. 2-8.
- Chapter Petrov D., Dodonova Y., Zhukov L. E., Belyaev M. Boosting connectome classification via combination of geometric and topological normalizations, in: PRNI 2016. The 6th International Workshop on Pattern Recognition in Neuroimaging. Trento, Italy, June 22nd – 24th, 2016. NY : IEEE, 2016. P. 1-4. doi
- Chapter Kurmukov A., Dodonova Y., Zhukov L. E. Classification of normal and pathological brain networks based on similarity in graph partitions, in: The Sixth IEEE ICDM Workshop on Data Mining in Networks. NY : IEEE Computer Society, [б.г.]. P. 1-6. doi
- Chapter Dodonova Y., Korolev S., Tkachev A., Petrov D., Zhukov L. E., Belyaev M. Classification of structural brain networks based on information divergence of graph spectra, in: 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP). NY : IEEE, 2016. doi doi
- Preprint Dodonova Y., Belyaev M., Tkachev A., Petrov D., Zhukov L. E. Kernel classification of connectomes based on earth mover's distance between graph spectra / Cornell University Library. 2016.
- Chapter Dodonova Y., Belyaev M., Tkachev A., Petrov D., Zhukov L. E. Yulia Dodonova, Mikhail Belyaev, Anna Tkachev, Dmitry Petrov, Leonid Zhukov. Kernel Classification Of Connectomes Based On Earth Mover’s Distance Between Graph Spectra, in BACON: Workshop on Brain Analysis using Connectivity Networks / MICCAI 2016, in: Proceedings of the 19th International Conference on Medical Image Computing and Computer Assisted Intervention, October 17-21, 2016, Athens, Greece, Springer. Athens : Springer, 2016. Ch. 5. P. 1-10.
- Chapter Курмуков А. И., Додонова Ю. А., Жуков Л. Е. Классификация расстройств аутистического спектра и нормального развития на основе сходства разбиений сетевых структур мозга // В кн.: Сборник статей конференции "Информационные технологии и системы" (ИТиС'16). М. : ИППИ РАН, 2016. С. 501-507.
- Chapter Petrov D., Dodonova Y., Zhukov L. E. Differences in Structural Connectomes between Typically Developing and Autism Groups, in: "Информационные технологии и системы 2015". St. Petersburg : Институт проблем передачи информации им. А.А. Харкевича РАН, 2015. P. 1-15.
- Chapter Korolev S., Zhukov L. E. Supervised Learning for Link Prediction Using Similarity Indices, in: "Информационные технологии и системы 2015" 39-я междисциплинарная школа-конференция 7 – 11 сентября, Олимпийская деревня, Сочи, Россия. St. Petersburg : Институт проблем передачи информации им. А.А. Харкевича РАН, 2015. P. 1-8.
- Chapter Додонова Ю., Петров Д., Жуков Л. Е. Сравнение эффективности ядер SVM-классификатора для различения пола на основе структурных коннектом // В кн.: "Информационные технологии и системы 2015" 39-я междисциплинарная школа-конференция 7 – 11 сентября, Олимпийская деревня, Сочи, Россия. St. Petersburg : Институт проблем передачи информации им. А.А. Харкевича РАН, 2015. С. 1-13.
- Article Zhukov L. E., Sukharev J., Popescul A. Learning Alternative Name Spellings // Information Retrieval. 2014
- Chapter Zhukov L. E., Sukharev J., Popescul A. Parallel corpus approach for name matching in record linkage, in: Proceedings of 14th International Conference on Data Mining (ICDM 2014). NY : IEEE Computer Society, 2014. P. 995-1000.
- Article Ignatov D. I., Kuznetsov S., Zhukov L. E., Poelmans J. Can triconcepts become triclusters? // International Journal of General Systems. 2013. Vol. 42. No. 6. P. 572-593. doi
- Chapter Жуков Л. Е. Профессия Data Scientist // В кн.: Большие Данные в национальной экономике. М. : ЗАО "Открытые системы", 2013. С. 48-51.
- Chapter Гнатышак Д. В., Игнатов Д. И., Жуков Л. Е., Кузнецов С. О., Миркин Б. Г. Экспериментальное сравнение некоторых алгоритмов трикластеризации // В кн.: Интеллектуализация обработки информации ИОИ: 9-я международная конференция. Черногория, г. Будва, 2012 г.: сборник докладов / Отв. ред.: К. В. Воронцов. М. : Торус Пресс, 2012. С. 609-612.
- Chapter Ignatov D. I., Kuznetsov S., Zhukov L. E. From Triconcepts to Triclusters, in: Rough Sets, Fuzzy Sets, Data Mining and Granular Computing: 13th International Conference, RSFDGrC 2011, Moscow, Russia, June 25-27, 2011. Proceedings / Ed. by S. Kuznetsov, D. Slezak, D. H. Hepting, B. Mirkin. Vol. 6743. Berlin, Heidelberg : Springer, 2011. P. 257-264.
