Biostatistics Faculty Research

Rebecca Betensky's research focuses on estimation, modeling and prediction based on survival data that features censoring, truncation and competing risks.  Students interested in working on a project should have taken a course in survival analysis and should have experience writing code for stimulation studies. 

Email Address: rebecca.betensky@nyu.edu

Our work focuses on the intersection of machine learning, data science, and public health. We’re particularly interested in questions around model performance and generalizability, analytic considerations for environmental and social determinants of health, and how to build models that reflect the complexity of real-world populations. We also host regular events on current topics in data science, aimed at fostering discussion and collaboration across disciplines.

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The mission of the Biostatistical Collaboration and Consultation Core (BC3) is: (1) to be a high-quality resource for robust, reliable, and reproducible statistical support; and (2) to educate and train the next generation of collaborative statisticians.  We support projects either through short-term periodic consultation meetings, or through long-term collaboration with our faculty and students. We work with investigators at every stage of the research process: proposal and grant development; hypothesis generation; study design; data validation and cleaning; statistical analysis; interpretation and visualization of results; and even addressing referee comments for a resubmission.

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The work of the Heng Lab focuses on causal inference theory and methodology, as well as their applications in public health, biomedical research, and social sciences.

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Statistics and machine learning, particularly change-point detection, sequential decision, streaming data analysis, longitudinal data, and their applications in engineering and public health.

Email Address: yajun.mei@nyu.edu

Research Area:

My research advances high-dimensional statistics, machine learning, and deep learning (a core area of AI) for analyzing complex imaging and omics data, with applications to complex human diseases such as Alzheimer’s disease, brain tumors, and breast cancer. My overarching goal is to develop and apply cutting-edge statistical and AI methodologies to effectively and efficiently analyze large-scale and/or multi-view data, improving the understanding, diagnosis, and prediction of complex diseases.

Email Address: hai.shu@nyu.edu

Research Area: 

My research is focused on applying advanced statistical methods to public health. This emphasizes the use of biostatistical tools such as causal inference, machine learning, data visualization, and structural equation modeling, with a particular interest in questions about treatment effects and moderated effects. My ongoing projects primarily focus on understanding tobacco use patterns and assessing the health effects of tobacco through analyses of large-scale observational data.

Email Address: violet.shuxu@nyu.edu

My group's research is focused on developing theory and methods for network data modeling, high dimensional statistics, econometrics, machine learning, and causal inference; particularly with applications in genomics, genetics, bioinformatics, protein structure modeling, and political science. Ongoing research projects include modeling network data for academic collaborations, faculty hiring dynamics, polarization based on legislation; single cell RNA-seq analysis, integration, spatial transcriptome, and protein mutation. The current research is funded through NSF and NIH.