Emily researches the capabilities and limitations of predictive machine learning algorithms in social services, with a focus on child and family policy. Her dissertation examines machine learning models that make predictions about individual people’s futures using two datasets: (1) the Future of Families and Child Wellbeing Study, an in-depth longitudinal survey that contains data on many important aspects of participants’ lives starting from birth, and (2) Dutch administrative register data, which contains demographic, economic, employment, school, and social services data from the entire population of the Netherlands. Emily has been developing expertise in child and family policy since her undergraduate years, through a self-designed undergraduate major in Human Development and Social Policy, an internship in public benefits and community resource navigation, two years as a research assistant at the child and family policy research organization Child Trends, and a job as committee assistant for the Health and Human Services Committee of the New Mexico House of Representatives. 

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