I am interested in building adaptive agents that can safely navigate complex environments to achieve desired outcomes. To this end, I am focussed on the framework of Bayesian model-based reinforcement learning. I believe it provides the natural basis for building robust and adaptive artificial agents that can deal with stochastic and partially-observable environments.
More specifically, my research has been dedicated to creating better world models (latent-variable generative modelling) and model-based agents with Bayesian planning objectives.
Bayesian sense of time in biological and artificial brains
Z Fountas*, A Zakharov* (*equal contribution)
Book chapter in Time and Science 2022, World Scientific Publishing
Geometric Deep Learning for Post-Menstrual Age Prediction
V Vosylius, A Wang, C Waters, A Zakharov, et al.
International Workshop on Graphs in Biomedical Image Analysis 2020