About the Talk: In the talk “Graph machine learning”, we cover the basics of machine learning on structural data, graph embedding, graph neural network techniques, efficient architectures for large-scale and dynamic networks, self-supervised learning on graphs, and application to computer vision and self-driving domain. The talk sums up all the current advances, starting from the idea of how to aggregate features from a given or designed metric space to how to provide an efficient framework for downstream tasks and robust applications.
About the Speaker: Ilya Makarov received a Specialist degree in mathematics from the Lomonosov Moscow State University, Moscow, Russia, and received a Ph.D. degree in computer science at the University of Ljubljana, Ljubljana, Slovenia.
Since 2011, he has been a Lecturer with the School of Data Analysis and Artificial Intelligence, HSE University, where he was the School Deputy Head, from 2012 to 2016, and is Associate Professor and Senior Research Fellow. In 2020-2022, he was also the Program Director of BigData Academy MADE from VK, and a Researcher at Samsung-PDMI Joint AI Center, St. Petersburg Department of V.A. Steklov Mathematical Institute, Russian Academy of Sciences, Saint Petersburg, Russia. He is also a Lecturer at the Moscow Institute of Physics and Technology and a Machine Learning Engineer and the Head of Data Science Tech Master Program in NLP at the National University of Science and Technology MISIS.
Now, he has occupied a Senior Research Fellow position at Artificial Intelligence Research Institute (AIRI), Moscow, Russia, where he leads the research in Industrial AI, and now Directs AI Center at MISIS University. His fields of interest are computer vision, industrial machine learning, augmented reality, and graph machine learning.