Location: |
Lecture Hall 205, , Xinyuan Building B Zone, South Campus, Xidian University |
Lecturer Profile |
EvgenyBurnaev obtained his MSc in Applied Physics and Mathematics from the Moscow Institute of Physics and Technology in 2006. After successfully defending his PhD thesis in Foundations of Computer Science at the Institute for Information Transmission Problem RAS in 2008, Evgeny stayed with the Institute as a head of IITP Data Analysis and Predictive Modeling Lab. Since 2007 EvgenyBurnaev carried out a number of successful industrial projects with Airbus, SAFT, IHI, and Sahara Force India Formula 1 team among others as a Head of Data Analysis group in Datadvancellc. Since 2016 EvgenyBurnaev is an Associate Professor of Skoltech and manages his research group for Advanced Data Analytics in Science and Engineering, specialized in industrial analytics and development of machine learning methods. The main research directions are predictive modeling for optimization of technological processes and 3D computer vision with medical applications. In 2017 EvgenyBurnaev (jointly with Alexey Zaytsev and Maxim Panov) was honored with the Moscow Government Prize for Young Scientists in the category "Transmission, Storage, Processing and Protection of Information" for his scientific contribution and leading the project "The development of methods for predictive analytics for processing industrial, biomedical and financial data." |
Lecture Abstract |
Modern diagnostics and prognosis of treatment in such areas as neurosurgery, neurology and psychiatry are impossible without the analysis of images obtained in different modalities: structural and functional magnetic resonance imaging (MRI), positron emission tomography (PET), as well as data on electrical activity of the brain (EEG). In psychophysiological studies of educational process and during training of cyber-sportsmen it is possible to use wearable electronics and eye tracking devices for data collectoin. Classical statistical analysis, as well as machine learning, applied to data with such complex structure and high dimensionality, is a challenge for data analysis engineers. In this presentation I will highlight the current state of the art in this field, achieved by the scientific group ADASE from Skoltech in such tasks as: effective dimension reduction of fMRI data for mapping the functional areas of the brain in surgical planning and neuronavigation, segmentation of three-dimensional MRI images to detect lesions, etc.
Key words: Deep Learning, NeuroImaging, fMRI, MRI, Medical Applications |
