Charles is broadly interested in how humans make decisions against a certain causal structure or representation of the world. Recently, the ubiquitous adoption of statistical learning algorithms has introduced a new causal context for studying the human learning-cum-decision-making process.
Charles' current research investigates - through both theoretical and empirical lenses - how machine learning interacts with human agency and organizational decision-making. He is also exploring the intersection of economics of AI, human causal reasoning and the problems of generalization and robustness.
Previously Charles worked as a commodities trader in Europe, the US and Asia.