Dmitry Vetrov, Research Professor and Head of the Centre of Deep Learning and Bayesian Methods at the HSE Faculty of Computer Science, is the first Russian scientist to be elected as a member to the ELLIS Society (the European Laboratory for Learning and Intelligent Systems), a leading European organization in the field of artificial intelligence.
Professor Vetrov was nominated by renowned AI scientist Max Welling of the University of Amsterdam. The ELLIS Society aims to maintain the human potential of the AI industry in Europe, since many of the top labs, as well as many of the top universities to pursue a PhD, are located in North America; moreover, AI investments in China and North America are significantly larger than in Europe.
According to Yann LeCun, Chief Scientist at Facebook AI and Joaquin Quinonero Candela, Director of Engineering at Facebook AI, ‘Europe has vibrant AI talent, and we're enthusiastic about the potential for ELLIS to further boost AI research in Europe and ensure a diverse ecosystem of experts around the globe.’
Being a member of the ELLIS Society, Dmitry Vetrov says, will provide more opportunities for students and doctoral students in the faculty. ‘Among other things, the association aims to facilitate exchange opportunities for doctoral students of ELLIS members. Joint scientific events will be held, and funding for research projects will be allocated. The ELLIS Society community is very impressive; currently, its members include Europe’s best scientists, and the association itself is based on the Canadian model. It is well known that Canada has become one of the world leaders in the field of artificial intelligence over the past 10-15 years. The inclusion of Russian members in ELLIS is, in any case, a good thing.’
Dmitry Vetrov graduated from Moscow State University in 2003 and became a candidate of sciences in 2006. He is the founder and head of a Bayesian methods research group, which has become one of the strongest research groups in Russia in the field of AI technology development. Three of his recent doctoral students have become DeepMind researchers. His research focuses on the application of Bayesian methods to deep learning models. His group is also actively involved in creating scalable tools for stochastic optimization, applying tensor decomposition methods to big data learning tasks, building cooperative multi-agent systems, and more.