Joshua Tan

Joshua Tan is Mathematician and AI researcher. Currently doing a PhD in computer science at Oxford. I am currently a doctoral student in computer science at Oxford studying under Samson Abramsky and Bob Coecke; previously, I completed my master’s in pure math at the Courant Institute at NYU, where my research involved applications of geometry and topology to artificial intelligence. For my thesis, I’ve been exploring different ways of applying category theory and sheaf theory to computational learning theory, from work on the sample compression conjecture to diversity measures in boosting. My interests include category theory, computational learning theory, sheaf theory, robotics, and art history. Currently: I help run the Metagovernance Project. We just won a flagship grant from the Grant for the Web. I’m working with Wistan Chou and Sokwoo Rhee on theoretical guarantees for cycle consistency. Paper coming soon. Michael Zargham and I just released Govbase, a database of projects and tools in online governance. I’m part of the executive team at Compositionality, a new peer-reviewed, open-access academic journal dedicated to compositional ideas in science and mathematics, especially those with a categorical origin. I edit a book series with Bob Coecke called Applied Category Theory, published by Cambridge University Press. Email us if you have an idea!