Keegan Hines is the VP of Machine Learning at Arthur, an ML model monitoring startup providing model observability and fairness auditing for the Fortune 100 and startups alike. He is also an Adjunct Assistant Professor in the Data Science Program at Georgetown University where he teaches courses in machine learning and deep learning. Keegan comes to Arthur from Capital One where he was the Director of Machine Learning Research and developed applications of ML to key financial services areas. He has also held roles at cyberdefense firms and is currently a Co-Founder and Chair of the Conference on Applied Learning for Information Security (CAMLIS). Keegan holds a PhD in Neuroscience from the University of Texas.