Vitaly is conducting research as a PhD student under the supervision of Professor Kody Law from the University of Manchester. Prior joining the Turing doctoral programme he obtained BSc and MSc in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology (MIPT), also he benefited by finishing the Data Science MSc track from the Skolkovo Institute of Science and Technology (Skoltech).
Vitaly has industrial research experience resulted in a number of R&D projects in mathematical modelling of wireless telecommunication networks, aspiring to combine and adapt the cutting-edge research breakthroughs with the industry needs.
Computer simulation models are widely used in many scientific and industrial applications. As a rule, these simulators, accurately describing the "physics" of the modelling system, are extremely expensive to run multiple times at high resolutions. Unfortunately, this is unavoidable while performing uncertainty quantification (UQ) tasks in a wide variety of scenarios. In order to overcome this obstacle, reduced-order models – so-called surrogates or emulators – are extensively employed, constructed on the datasets (labelled input-output pairs) obtained from the original simulator or full-scale experiment.
In his research, Vitaly is aiming to contribute to the development of both novel UQ methods for neural networks and adaptive experimental design techniques that will allow constructing accurate surrogate models having used fewer data from the initial simulation model. He also is eagerly seeking for the industrial (but not only!) collaboration on solving challenging problems.