Alex is a PhD candidate in the Statistics department at UC Berkeley. His research focuses on explainability, fairness, and auditing in machine learning. Most recently, he has worked on leveraging longitudinal data to improve feasibility in counterfactual explanations. Alex also was a member of the inaugural cohort of the AI Policy Hub, where he worked on policy proposals regarding explanations from AI decision-makers. Prior to his PhD program, Alex received a Bachelor's degree in Mathematics from Howard University.