Patrick Hall is principal scientist at HallResearch.ai. He is also teaching faculty at the George Washington University (GWU) School of Business, offering data ethics, business analytics, and machine learning classes to graduate and undergraduate students. Patrick conducts research in support of NIST's AI Risk Management Framework, works with leading fair lending and AI risk management advisory firms, and serves on the board of directors for the AI Incident Database.

Prior to co-founding HallResearch.ai, Patrick was a founding partner at BNH.AI, where he pioneered the emergent discipline of auditing and red-teaming generative AI systems; he also led H2O.ai's efforts in the development of responsible AI products, resulting in one of the world's first commercial applications for explainability and bias management in machine learning. 

Patrick has been invited to speak on AI and machine learning topics at the National Academies, the Association for Computing Machinery SIG-KDD ("KDD"), and the American Statistical Association Joint Statistical Meetings. His expertise has been sought in the New York Times and NPR, he has been published in outlets like Information, Frontiers in AI, McKinsey.com, O'Reilly Media, and Thomson Reuters Regulatory Intelligence, and his technical work has been profiled in Fortune, WIRED, InfoWorld, TechCrunch, and others. Patrick is the lead author of the book Machine Learning for High-Risk Applications.

Dr. Agus Sudjianto is the Senior Vice President, Risk and Technology for Enterprise at H2O.ai. He brings over two decades of experience in the financial services industry, with leadership roles in risk management, analytics, and modeling at Wells Fargo and Bank of America. Agus is renowned for pioneering PiML (Python Interpretable Machine Learning), a set of methods and tools for creating interpretable and understandable machine learning models. He has championed the adoption of PiML in the industry, open-sourcing it to democratize models for ensuring reliability, resilience, and fairness in high-risk applications. At H2O.ai, Agus focuses on developing H2O Eval products to address AI safety, reliability, and compliance, with a focus on Generative AI applications in banking and model risk management. Agus holds a PhD in Engineering from Wayne State University and a Master’s degree from MIT.