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Emma Zang is an Assistant Professor in the Department of Sociology at Yale University, with secondary appointments in Biostatistics and Global Affairs. She completed her Ph.D. in Public Policy in 2019 and her MA in Economics in 2017, both from Duke University. Her research interests intersect health and aging, family demography, and inequality, with a particular focus on examining these dynamics in both the United States and China. She is interested in developing and evaluating methods to model trajectories and life transitions, aiming to understand the impact of demographic and socioeconomic inequalities on individuals' health and well-being from a life course perspective. Her research primarily focuses on employing Bayesian approaches to model trajectories and construct multi-state life tables using high-dimensional survey data. Additionally, she evaluates classic sociological methods, including the Age-Period-Cohort Intrinsic Estimator and the Diagonal Mobility Model.

What do you do with Data Science?

My work focuses on health inequality and developing user-friendly methodologies. With co-authors, I’ve created two innovative methods: Bayesian multistate life tables (Sociological Methodology 2022) and Bayesian group-based trajectory models (Psychological Methods 2022). Our Bayesian multistate life table method extends the Bayesian approach to high-dimensional state spaces with partially absorbing states. This method enables us to examine geographical, racial/ethnic, and educational disparities in the impact of conditions like diabetes on population health. In another project, we’ve developed a Bayesian approach for group-based trajectory models, improving variable selection through model averaging. This efficient technique enhances predictions and saves researchers’ time. We’ve applied these models to study linked health trajectories of persons with cognitive impairment and their caregivers (e.g., Age and Ageing 2022). These methodologies contribute to better understanding of health disparities and trajectories. Additionally, I am involved in evaluating classic sociological methods, including the Age-Period-Cohort Intrinsic Estimator (American Journal of Sociology 2023) and the Diagonal Mobility Model (Social Science Research 2023). In the next five years, I aim to expand my research on how institutions influence health disparities and household dynamics, with a special focus on policy evaluations and data science. My ongoing projects include using big data and computational methods to analyze COVID-19’s impact on health and family dynamics. We’ve developed a social distancing score based on millions of mobile device data and are examining the role of social media in the Black Lives Matter protests’ influence on social distancing behaviors. We are currently engaged in an ongoing project that assesses the effectiveness of various cutting-edge large datasets. These datasets encompass a wide range of sources, such as cell phone location data, Zillow housing price information, nighttime illumination data, USPS change of address records, voter registration data, and credit card transaction records. Our objective is to utilize these datasets to enhance our modeling of migration patterns within the United States. Moreover, I am committed to advancing the field of health inequality research by integrating genetic expertise, adopting an environmental perspective, and refining trajectory modeling methods. This includes the utilization of cutting-edge techniques such as Gaussian processes within the framework of machine learning. Zang, Emma., Michael Sobel, Liying Luo. (2023). "The Mobility Effects Hypothesis: Methods and Applications." Social Science Research 110:102818. Zang, Emma., Justin T. Max. (2022). "Bayesian Estimation and Model Selection in Group-Based Trajectory Models." Psychological Methods 27(3):347-372. Lynch, Scott, Emma Zang. (2022). "Bayesian Multistate Life Table Methods for Complex, High-Dimensional State Spaces: Development and Illustration of a New Method." Sociological Methodology 52(2): 254–286. Fu, Qiang., Xin Guo, Sun Jeon, Eric Reither, Emma Zang, Kenneth Land. (2021). "The Uses and Abuses of An Age-Period-Cohort Method: On The Linear Algebra and Statistical Properties of Intrinsic and Related Estimators." Mathematical Foundations of Computing 4(1):45-49. Land, Kenneth., Qiang Fu, Xin Guo, Sun Jeon, Eric Reither, and EmmaZang. (2016). "PlayingWith the Rules and Making Misleading Statements: A Response to Luo, Hodges, Winship, and Powers." American Journal of Sociology, 122 (3): 1-12.

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