Research Labs


The goal of the Biomedical Statistical Science Laboratory (BSSL) is to develop statistical methods for noncompliance in surgical clinical trials, covariate measurement error for nonlinear regression models and image based research. The BSSL is directed by Dr. Tor Tosteson.


The goal of the Mathematical Biostatistics and Image Analysis Laboratory (MBIAL) is to develop mathematical and biostatistical methods for the analysis of medical images and shapes. Other areas of interest include mixed models, sample size and power calculations, asymptotic hypothesis tests comparison, optimization in statistics, image reconstruction, inverse problems, financial mathematics, partial differential equations and tumor response to treatment. The MBIAL is Directed by Dr. Eugene Demidenko.

This lab focuses on developing statistical mediation modeling approaches to identify disease causal pathways mediated by microbes and epigenetic changes in molecular epidemiology and children’s environmental health research. More specifically, we are interested in baby microbiota and epigenetic changes mediating the effects of prenatal and postnatal factors such as in utero arsenic exposure, maternal diet and postnatal arsenic exposures on their health outcomes including infection, allergy, atopy, etc. This lab is also interested developing joint modeling approaches to account for the dependence between quality of life and survival when analyzing such data in palliative care research. The Microbiome Mediation Modeling Lab is directed by Dr. Zhigang Li.


The goal of the Population Health Laboratory (PHL) is to promote population-based health and delivery of health care that is most effective for clinical, socio-demographic, and geographic groups. We have a strong focus in cancer care, including screening, treatment, and surveillance. We believe that how and where care is received influences treatment and outcomes. With a special emphasis in how health care resources are allocated across populations, the PHL uses cross-disciplinary, geospatial innovation in data-driven research to address critical questions related to population health and health care delivery. The PHL research program is largely built around our expertise in the integration of population health, health services research, registries, geospatial data, Geographic Information Systems (GIS), and informatics. The PHL is directed by Dr. Tracy Onega. Dr. Onega also co-leads the Registry Shared Resource of the Norris Cotton Cancer Center.


The goal of the Social Computing & Health Informatics (SCHI) Lab is to advance analytics and tools for collaborative intelligence in healthcare and biomedical research through novel social computing, machine learning, and data visualization methods. The SCHI Lab is directed by Dr. Amar Das.

The goal of the Statistical Genetics Laboratory (SGL) is to understand human health and disease through the development and application of statistical methods for identifying genetic risk factors. The SGL is interested in the genetic epidemiology of lung and colon cancer and the gene mapping of causal factors and modifier loci for rare syndromes including Lynch syndrome, Peutz-Jeghers syndrome and Li-Fraumeni syndrome. The SGL is also interested in the genetic epidemiology of select autoimmune conditions including rheumatoid arthritis, primary biliary cirrhosis, and alopecia areata. The SGL facilitates these studies through the collection and management of data from family studies and is interested in the design of clinical studies to identify predictors of cancer development and progression. The SGL is directed by Dr. Chris Amos.

The goal of the Statistical Genomics Laboratory (SGL) is to develop cutting-edge biostatistical methods for the analysis of high-dimensional omics data. Recent work has focused on the detection of gene-gene interactions in genome-wide association data. The SGL is directed by Dr. Jiang Gui.

The general theme of this lab is the development of innovative solutions to novel methodological and applied problems in statistics. In so doing, we seek to push the frontier of statistics in practice. Current domains of focus and example projects include:

  • Social network analysis: The development and application of social networks to gain insights into the diffusion of medical technology and practices; The development of models for relational data in longitudinal and hierarchical contexts.
  • Multivariate-multilevel models: The development of methods to estimate the covariance matrix of a multivariate outcome at a group level (e.g., health plan or hospital) when observations are obtained from sub-units (e.g., patients).
  • Causal inference: The development of methods to distinguish social influence (peer effects) from social selection (homophily); The development of methods for causal inference in randomized trials with departures from the study protocol and in observational studies subjected to unmeasured confounding when the outcome is a possibly censored time-to-event.
  • Proximity to food establishments and neighborhood effects: The application of a novel model to estimate the effect of proximity to various type of food establishments on health outcomes accounting for neighborhood effects and other forms of heterogeneity and clustering of individuals.

The Statistical Problem Solving Laboratory is directed by James O’Malley.