Together, we can shape the future of model operations while optimizing ML models for accuracy, explainability, and fairness to ensure compliance in highly regulated industries.
From the lab to the boardroom, we partner with global data scientists, ML directors and AI Center of Excellence leadership to launch real-world solutions worldwide. As enterprises embark on their AI maturity journey, we share researcher insights, advance whiteboard ideas, empower best practices, benchmark industry metrics, and inspire thought leadership.
John solves problems at the intersection of economics and artificial intelligence using techniques from machine learning, stochastic optimization, and computational social choice with a focus on healthcare. Grant work includes NIST, DARPA, ARPA-E, NIH (R01), NSF, and Google.
Assistant Professor in the Department of Computer Science at the University of Maryland
Computer Science PhD, Carnegie Mellon
Keegan's previous roles included Capital One where he was the Director of Machine Learning Research and developed applications of ML to key financial services areas.
Adjunct Assistant Professor in the Data Science Program at Georgetown University, Machine Learning & Deep Learning
Neuroscience PhD, University of Texas
Co-Founder & Chair, Conference on Applied Learning for Information Security (CAMLIS)
Jessica's research interests include fairness, auditing, and characterizing and understanding the behavior of machine learning models.
In addition to her work at Arthur and Brown, Jessica has previously collaborated with researchers at Carnegie Mellon and Harvard.
Computer Science ScB, Brown University
Fair machine learning: academic literature & application settings
Machine learning and policy
Kweku is broadly interested in machine learning and statistics with a specific focus on the design of algorithms that audit machine learning models for fairness and robustness. He is interested in questions which rigorously examine and critque data-driven technological solutionism.
PhD Candidate in the Brown University Department of Computer Science
BS Computer Science and Mathematics at University of Maryland
Auditing machine learning algorithms in real world settings
From academic publishing to real world practice, learn how Humana and Arthur worked together to transform the third largest health insurance provider in the nation.