Department of Intelligent Systems
Phystech School of Applied Mathematics and Informatics MIPT
The base department graduates bachelors and masters in the direction 010900 “Applied Mathematics and Physics”. Education at the department is in three specializations “Data Science”, “Design and Organization of Intelligent Systems”, “Information Retrieval and Machine Learning”. The base organization is the Computing Center of the Federal Research Center “Informatics and Control” of the Russian Academy of Sciences.
The base organization is the Computing Center of the Russian Academy of Sciences, the Federal Research Center “Informatics and Control” of the Russian Academy of Sciences. The founder is an academician of the Russian Academy of Sciences and an outstanding Russian mathematician, an expert in the field of mathematical methods of recognition, forecasting and data analysis Konstantin Vladimirovich Rudakov. The department has been actively developing since 2003 within the scientific school of the academician of the Russian Academy of Sciences Yuri Ivanovich Zhuravlev. A significant contribution to the development of the department was made by the professor of the Russian Academy of Sciences, Doctors of Physical and Mathematical Sciences. K.V. Vorontsov and V.V. Strijov.
Teachers of the department
20 researchers: professor of the Russian Academy of Sciences, doctors and candidates of sciences. Teachers are young candidates of sciences, graduates of the department Ph.D. Alexander Aduenko, Ph.D. Oleg Bakhteev, Ph.D. Roman Isachenko, graduate student Andrey Grabovoy. The average age of the teachers is 35 years.
Areas of educational and scientific activities of the department
Machine learning; multivariate statistics; geometric methods of deep learning; model selection and neural network architectures; ensembles of models and generative neural networks; functional data analysis and analysis of spatio-temporal data.
Text analysis and topic modeling, image and video analysis, multivariate time series analysis, biomedical signal analysis, brain-computer interfaces.
Principles of Scientific Work
Openness of ideas, projects, results at all stages of study and research; continuous assessment of the quality of ideas and results; communication with the scientific community, updating according to the latest achievements.
Machine Learning, read by K.V. Vorontsov, is the most famous and complete course in Russia, covering fundamental and modern topics of machine learning and data analysis.
My first scientific article: automation of scientific research, read by V.V. Strijov, is a course that provides the basics for performing scientific research - from planning to presenting results. Each student chooses a personal project and receives a personal consultant and expert. For three months, the student prepares a scientific paper with theory and computational experiment. The results are presented at scientific conferences, articles are submitted to peer-reviewed journals.
Main courses (programs)
- Machine Learning
- Automation of scientific research
- Deep learning
- Bayesian model selection
- Forecasting methods
- Deep generative models
- Bayesian multimodeling
- Probabilistic topic modeling
- Natural language processing
- Data analysis in metric spaces
- Signal processing
- Software engineering for data analysis
- Recommender systems
- Scientific academic scholarship to them. K.V. Rudakova is awarded to undergraduate and graduate students for academic and research excellence. Sponsored by Forexis Group.
Collaborative training programs
In 2019, the department organized a double degree and joint master’s program at the University of Grenoble-Alpes. Master’s students study in joint programs at the Paris Polytechnic School, the Skolkovo Institute of Science and Technology, the KAUST University of Science and Technology. Joint research is underway with laboratories UGA, INRIA, CNRS, Ecole Polytechnique, EPFL, Los-Alamos, CMU.
- Master program in Operations Research, Combinatorics and Optimization, MIPT page
- Master of Science in Informatics at Grenoble, MIPT page
- Cycle Ingeneur at Ecole Polytechnique, MIPT website
Since the beginning, the department has been actively cooperating with the base companies of the Forexis Group of Companies: Forexis, Antiplagiat, Antirutina, GoodfoCast, Labor Laboratory, Procompliance and participates in joint projects of Forexis Group with the companies MMFB, InterRAO, Yandex, Sberbank, Russian Railways, Samsung, LG.
- Forexis: time series analysis, financial analytics, news flow analysis
- Antiplagiat: text analysis
- INRIA National Institute for Research in Digital Science and Technology: Bioinformatics