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 (IITP 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. The corresponding data analysis algorithms, developed by Evgeny Burnaev and his scientific group, formed a core of the algorithmic software library for metamodeling and optimization. Thanks to the developed functionality, engineers can construct fast mathematical approximations to long running computer codes (realizing physical models) based on available data and perform design space exploration for trade-off studies. The software library passed the final Technology Readiness Level 6 certification in Airbus. According to Airbus experts, application of the library “provides the reduction of up to 10% of lead time and cost in several areas of the aircraft design process”. Nowadays a spin-off company Datadvance develops a Software platform for Design Space Exploration with GUI based on this algorithmic core.
Since 2016 Evgeny Burnaev works as Associate Professor of Skoltech and manages his research group for Advanced Data Analytics in Science and Engineering
For his scientific achievements in the year 2017 Evgeny Burnaev (jointly with Alexey Zaytsev and Maxim Panov) was honored with the Moscow Government Prize for Young Scientists in the category for the Transmission, Storage, Processing and Protection of Information for leading the project “The development of methods for predictive analytics for processing industrial, biomedical and financial data.”
Today, Evgeny’s research interests encompass the areas of regression based on Gaussian processes and kernel methods for multi-fidelity surrogate modeling and optimization, Deep Learning for 3D Data Analysis and manifold learning, on-line sequence learning for prediction and non-parametric anomaly detection. The main aim is development of new machine learning methods and their use for solution of applied engineering problems.
ФИО: Бурнаев Евгений Владимирович
Занимаемая должность: Доцент
Ученая степень: Кандидат физико-математических наук, 2008, Институт проблем передачи информации им. А.А. Харкевича РАН
Наименование направления подготовки и/или специальности: Теоретические основы информатики
Данные о повышении квалификации и/или профессиональной переподготовке (при наличии): Летняя школа по машинному обучению, 2015, Институт интеллектуальных систем им. Макса Планка, Тюбинген, Германия
Общий стаж работы: 10 лет
Стаж работы по специальности: 10 лет