Toyya Pujol (she/her) is an operations researcher at RAND. Her work is in health data science with an emphasis on maternal health, mental health (including substance abuse), and health disparities. Her skillset includes statistics, machine learning, and network science. Previous research projects include evaluating contraception trends for women with chronic health conditions, assessing the impact of teen pregnancy on infant outcomes using claims data, developing a double robust machine learning method for difference-in-differences study design, and estimation of edge weights for health care networks. Before her Ph.D., she worked as a cost analyst for the Air Force including positions at AFCAA, Hanscom AFB, and LA AFB.  Since joining RAND she has worked on projects in causal inference, applied statistics and simulation with applications to the Opioid epidemic, Homeland Security, Air Force workforce, and healthcare policy.

Pujol received her Ph.D. in industrial engineering from the Georgia Institute of Technology.

Education

Ph.D. in industrial engineering, Georgia Institute of Technology-Main Campus; M.S. in statistics, Georgia Institute of Technology-Main Campus; M.S. in operations research, Northeastern University; B.S. in management science, Massachusetts Institute of Technology

Authored by Toyya Pujol-Mitchell

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