Ryan studies epistemic infrastructure. Digital artifacts like Wikipedia, arXiv, and Reddit inform much of our worldviews, our public discourse, and our state-of-the-art AI systems. They are complex, networked, dynamic, linguistic, and politicized artifacts. How do we construct these social systems of knowledge, and what coordination mechanisms ensure their proper function? How do failure modes like disinformation, echo chambers, and bias arise, and how can we combat them?
Ryan previously studied Philosophy (BAH) and Computer Science (BS) at Stanford. His undergraduate honors thesis in Philosophy formulated a theory of trust for evaluating explainable AI systems. He received Stanford’s 2022 Suppes Award for Excellence in Philosophy. He also worked as a Founding Data Scientist at Monte Carlo Data, published research with the Association for Computational Linguistics, and wrote three chapters of the O’Reilly textbook Data Quality Fundamentals.
Natural Language Processing, Computational Linguistics, Multi-Agent Systems, Trustworthy AI, Explainable AI, Social Epistemology, Social Cognition, Computational Social Choice, Network Science, Network Dynamics.
