Faculty and Staff

The Attachment and Health Disparities Lab seeks to understand health disparities observed among young sexual and racial/ethnic minorities through the lens of Dr. Cook’s integrated theory of adult attachment and minority stress.

Though current theoretical paradigms of attachment indicate how individuals respond to stress, these theories do not adequately account for the unique impact of social stressors on individual health and well-being, which may be of critical importance in understanding the drivers of health in marginalized populations. The negative social valuation of a marginalized identity—such as a sexual minority identity or a racial minority identity—causes stress in persons with a marginalized social status beyond the level of stress that people generally experience; this excess stress has been named minority stress.

However, many theories of minority stress are limited and inadequately delineate the associations between attachment orientation, stress, and subsequent health outcomes.  Making these theoretical and empirical linkages is important for understanding how to address health disparities among disadvantaged individuals who are at heightened risk for experiencing minority stress compared to other individuals (e.g., African-American youth, sexual minority men). Therefore, one of the main objectives of Dr. Cook’s research is to understand the pathways and mechanisms located particularly at the intersection of marginalized identities that link attachment, minority stress, and health among disadvantaged individuals.

The biostatistics consulting lab (also known as GPH-GU 3235 Biostatistical Consulting when offered as a formal course), led by Dr. Rebecca Betensky, is an initiative in which students work to provide statistical support on real-world studies being performed throughout the NYU community, with our predominant partnerships taking place with researchers at the School of Medicine and GPH. In addition to experience working with real data, these partnerships can also lead to co-authorship and summer research opportunities.

For more information on upcoming meetings (or for requesting statistical support on a study), please visit our Consulting Lab page.

The overarching goal of our research is to develop computational and statistical approaches for acquiring, integrating and using data to improve population-level public health. We focus on the design and development of data mining and machine learning methods to address challenges related to data and goals of public health, as well as fairness and ethics in the design and use of data and algorithms embedded in social systems.

Visit the Chunara Lab website here. 

Led by Dr. Yang Feng in the Department of Biostatistics at CGPH, the Feng Lab seeks to develop and apply machine learning (ML) and big data methods to solve public health problems.  In addition, Feng Lab is interested in high-dimensional data analysis and modeling, network models, nonparametric and semiparametric methods, and bioinformatics. 

Feng Lab is actively looking for motivated talents at undergraduate, master and Ph.D. levels. If you are interested, please submit an application here.

Measurement, Learning, & Evaluation (MLE) lab director Melody Goodman and the multidisciplinary team members are committed to developing, implementing, and evaluating specific solutions to address measurement gaps in research and practice to address health disparities. As a biostatistician and health disparities methodologist, Dr. Goodman challenges her team members to work collaboratively with community health stakeholders to address pressing issues affecting the health of minority and medically underserved communities. The MLE lab conducts mixed-methods (qualitative/quantitative) community-engaged research focused on rigorous measurement, engages in scientific learning using data-driven approaches, and implements comprehensive (formative, summative, impact) evaluation.

MLE has two primary research tracks: 1) an applied methods track with an emphasis on survey research and a strong focus on measurement/measure development, and (2) a community-engaged research track with a focus on enhancing the infrastructure for community-engaged research through academic-community collaborations and through the development, implementation, and evaluation of community-engaged research projects and programs to reduce health disparities. In addition, MLE conducts collaborative work to support research teams/community-academic partnerships with study design, survey instrument development, data management, statistical analysis, and program/project evaluation.

Visit the MLE Website

The Xu Quantitative Lab led by Dr. Shu Xu focuses on the application and evaluation of innovative quantitative methods to social science and public health research. Our mission is to reinvent the public health paradigm by inspiring innovative scholarship, practice and leadership across boundaries. The primary research track of this lab is (but not limited to) tobacco use and health behavior change using Population Assessment of Tobacco and Health (PATH) data; and is centered on various aspects of latent growth models, missing data methods, causal inference models, and developing statistical methods for longitudinal data analysis. We work to understand and address the health effects of tobacco use; assess the effects of tobacco use patterns on various health outcomes; tobacco cessation and control strategies; effects of tobacco on various population groups; assess and evaluate the tobacco campaigns and policies.

For more information, please contact Dr. Shu Xu at sx5@nyu.edu.