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News

Interdisciplinary Seminar of the Strategic Academic Unit 'Mathematics, Computer Science, and Information Technology'

A regular research seminar aimed at sharing the results of research conducted as part of of the Strategic Academic Unit ‘Mathematics, Computer Science, and Information Technology’ and determining prospective interdisciplinary fields was recently held at HSE. This seminar will be organized regularly by different departments within the Strategic Academic Unit.

The first seminar was held at HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM HSE). Research fellows from laboratories of the Faculty of Computer Science gave a presentation to MIEM students and lecturers on process analysis, medical informatics, and machine learning and its application to the study of physical phenomena. In April the researchers from MIEM will present their projects to students and staff of the Faculty of Computer Science.

Process mining — methods of synthesis and analysis of process models

Process mining is a discipline that combines the techniques of data analysis, statistical and probabilistic methods, and algorithms of formal modeling and analysis. The discipline focuses on process discovery from ‘event logs’ that store information about system events. These event logs are managed by modern information and software systems for archiving, documenting, and debugging. At the seminar Alexey Mitsyuk, Junior Research Fellow at the Laboratory of Process-Aware Information Systems, gave a brief overview of process mining as a field of study. He paid special attention to the process enhancement and the instruments used by the author in his studies at PAIS Lab.

Research in medical informatics

In his report Sergei Kuznetsov, Head of the International Laboratory for Intelligent Systems and Structural Analysis, made a brief overview of the research conducted at the laboratory and analyzed several projects and tasks in the field of medical data analysis.

Machine learning in the separation of neutral particles in the LHCb calorimeter

A calorimeter, a piece of apparatus for measuring the loss of energy when particles pass through a medium, is used for detecting uncharged particles. Due to the low resolution of the device, two passing particles can be mistaken for one. Methods of machine learning can be applied to separate double and single particles. Victoria Chekalina from the Laboratory of Methods for Big Data Analysis spoke about the approach that uses the form of the response as a splitting characteristic, and also described an approach that uses all the information about the response.

Deep learning and Bayesian methods

Over the last few years the world has seen the rapid development of technologies based on neural networks. These methods, also called ‘deep learning’, helped to solve many problems which, for decades, were considered impossible for a computer, for example, understanding image content, speech recognition, and playing the game of ‘Go’ at a professional level. Michael Figurnov, Research Fellow at the International Laboratory of Deep Learning and Bayesian Methods, presented research on deep learning (acceleration and compression of neural networks) conducted by the staff of the laboratory. He also spoke about the way Bayesian methods help to solve more complex problems which cannot be solved by neural networks alone. For example, Figurnov analysed the work of the lab’s staff on the discernment of words’ meanings in the context and thinning of neural networks.