Faculty Research Projects

My research sits at the intersection of biostatistics, epidemiology, and social genomics. Using large population-level datasets, I study how psychosocial exposures can become biologically embedded through immune and genomic pathways. My methodological work focuses on cluster-weighted, semiparametric approaches for longitudinal data with informative cluster sizes.

BASE Lab Website | ReCAP Lab Website

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 in this area should have taken a course in survival analysis and should have experience writing code for stimulation studies.  She also focuses on novel designs and endpoints for clinical trials, such as the win ratio.

Email Address: rebecca.betensky@nyu.edu

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.

Website

The Feng Lab develops statistical learning theory, methodology, and software for high-dimensional and heterogeneous data. Our four main directions are transfer and multi-task learning, including its high-dimensional, unsupervised, federated, and privacy-preserving variants; Neyman-Pearson classification for asymmetric error control; social network analysis, covering community detection, multi-layer networks, network covariates, and longitudinal models; and high-dimensional variable screening. Methodological work is released as open-source R packages on CRAN, and applied through collaborations in oncology, neuroimaging, and tobacco research. The group emphasizes rigor, creativity, and cross-disciplinary engagement, and welcomes students at both the research-experience and thesis stages.

Feng Lab (Data Science & AI)

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.

Website

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.