Observational and causal learning

How do we learn causal relations in the world? How can methods in AI and machine learning perform causal reasoning in the same way we do? The field of causal inference deals with how the effect of interventions on the world can be predicted from observation. I am interested in problems at the intersection of causal inference and reinforcement learning, both from a statistical point of view and as it may relate to how we (and some other animals) perform observational causal learning.

Causal inference in neuroscience

More generally I am interested in developing causal inference methods that can be appied to neural and other biomedical datasets. I am interested in projects that aim to provide causal explanations, meaning they study and predict what will happen under intervention. In neuroscience, optogenetics and large-scale neural recording technologies increasingly make this possible. It is an exciting time to be in the field, where we are now better able to move away from giving associations between stimulus, neural activity and behavior, to giving causal mechanisms that relate stimulus, neural acivity and behavior.

In the past I have worked on a diverse range of projects in computational neuroscience, from neural data analysis of primary motor activity to computer vision problems related to neuron tracking in freely moving cnidaria (Hydra). Before my doctoral studies, my interests were in systems biology and bioinformatics.