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机器学习系列报告(二)之生物医学应用: Neurotechnologies and machine learning: from neurosurgery and neurology to neuroeducation and cybersport
时间:2019-03-17 15:56    点击:   所属单位:数学与统计学院
讲座名称 机器学习系列报告(二)之生物医学应用: Neurotechnologies and machine learning: from neurosurgery and neurology to neuroeducation and cybersport
讲座时间 2019-03-19 16:00:00
讲座地点 西电南校区信远楼II区205报告厅
讲座人 Prof.Evgeny Burnaev
讲座人介绍
Evgeny Burnaev 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 Evgeny Burnaev 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 Datadvance llc. Since 2016 Evgeny Burnaev 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 Evgeny Burnaev (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."
  注: Burnaev教授所在斯科尔科沃科技研究院与MIT联合举办2019机器学习暑期学校,目前开放学生和老师注册,具体信息详见:https://www.skoltech.ru/en/2019/03/machine-learning-summer-school-2019-at-skoltech/
http://mlss2019.skoltech.ru  
https://facebook.com/SkoltechMLSS/
讲座内容
  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

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