My research in a nutshell
To build a robot that learns and makes decisions, what algorithms would we need?
My research in a coconut
Based on established neurobiological correlates of reinforcement learning (RL), I investigate animal learning and decision making using cognitive modeling techniques, such as probabilistic programming and machine learning. Animals somehow manage to create useful representations of incoming sensory information, representations then used for learning and decision making. How these representations of states of the world are integrated into task structure and models of the world is an open question, which I investigate using behavioural experiments with humans and bumblebees and modelling said behaviour using RL combined with hidden state models for representing states and task structure. The potential findings of these experiments have promise to not only elucidate the workings of the animal brain but also provide valuable contributions to artificial intelligence, where improved models of state representations could vastly improve data efficiency and generalizability over current generation systems.
My research in papers
- Towards human-like artificial intelligence using StarCraft II
(2018, FDG Conference, Peer-reviewed)
- The Effect of State Representations in Sequential Sensory Prediction: Introducing the Shape Sequence Task
(2020, CogSci Conference, Peer-reviewed)