Bogdan Zviazhynski

Research

Machine learning is a class of methods that rely on existing data to train a model and then predict a quantity of interest. Some machine learning methods can also estimate the uncertainty in those predictions. My research program is pioneering the exploitation of this uncertainty as an input for the second machine learning model to predict a different and final physical property.

This generic technology should find an application across a broad range of physical and biological systems. For example, motivated by Renormalization Group Theory, in which fluctuations determine the macroscopic state of the system, the methodology could predict heat capacity of new materials. A second example could be the replication, growth, and death of cells in the human body.