Logan is fascinated by abstract representations. Abstractions provide a basis for generalization, reasoning, inferences from sparse data, etc. Bayesian probabilistic models capture abstraction well, but it is less clear how neural networks could embody and manipulate such abstractions. He hopes to illuminate this problem through novel ways of visualizing and probing neural networks, and by drawing from philosophical concepts regarding the structure of beliefs and knowledge. He can be reached at [email protected].