Population Data Science and Methodology

CPIP integrates expertise across econometrics, machine learning, social network analysis, and intensive longitudinal data analysis to generate new discoveries and train the next generation of data and population scientists. Core faculty have developed new methods for collecting and analyzing complex data across a wide range of fields. CPIP is uniquely positioned to have a transformative and sustained impact on the field by integrating three core areas of expertise that will advance science, strengthen causal inference, and create a model for training the next generation of data scientists: (1) the linkage of large-scale population data derived from education, health, school and neighborhood settings; (2) the integration of machine learning and econometrics to analyze large and information-rich data sets; and innovation with respect to the (3) the collection and analysis of intensively gathered mobile, wearable, sensor and geo-spatial data.

Faculty: Marion Aouad, Vellore Arthi, Damon Clark, Matthew Freedman, Rachel Goldberg, Matthew Harding, John Hipp, Suellen Hopfer, Jade Jenkins, Meera Mahadevan, David Neumark, Candice Odgers, Emily Owens, Andrew Penner, David Schaefer, Aryana Sepassi, Naomi Sugie