Research
I am interested in building adaptive agents that can safely navigate complex environments to achieve desired outcomes. To this end, I am focused 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 modeling) and model-based agents with Bayesian planning objectives.
Publications

Modelling non-reinforced preferences using selective attention
NeurIPS 2022, WiML

Bayesian sense of time in biological and artificial brains
Book chapter in Time and Science 2022, World Scientific Publishing

Episodic memory for subjective-timescale models
ICML 2021, WURL

Geometric Deep Learning for Post-Menstrual Age Prediction
International Workshop on Graphs in Biomedical Image Analysis 2020