Daniel Brown, PhD
About Daniel Brown, PhD
Daniel Brown, PhD, serves as the Principal Machine Learning Scientist at Carelon in Palo Alto, CA, where he focuses on using big data to enhance the healthcare system. His research interests encompass causal inference, social determinants of health, occupational epidemiology, and health policy.
Work at Carelon
Daniel Brown serves as Principal Machine Learning Scientist at Carelon, a position he has held since 2021. Based in Palo Alto, CA, he focuses on applying machine learning techniques to enhance healthcare outcomes. His role involves leveraging big data to inform health policy and improve the overall healthcare system.
Education and Expertise
Daniel Brown holds a Doctor of Philosophy (PhD) in Biostatistics from UC Berkeley School of Public Health, which he completed from 2009 to 2014. He also earned a Master of Public Health (MPH) in Biostatistics from Columbia University - Mailman School of Public Health, studying from 2004 to 2006. His undergraduate education includes a Bachelor of Arts (BA) in Chemistry from Pomona College, completed from 1997 to 2001.
Background
Before joining Carelon, Daniel Brown worked as a Data Scientist at Apple from 2014 to 2018 in Cupertino, CA. He also gained experience as a Graduate Student Researcher at UC Berkeley School of Public Health, where he worked from 2009 to 2017. His diverse background in both industry and academia informs his current research and professional focus.
Research Interests
Daniel Brown's research interests encompass causal inference, social determinants of health, occupational epidemiology, and health policy. He is particularly focused on utilizing big data to drive improvements within the healthcare system, aiming to address various health-related challenges through data-driven insights.