Leadership in Public Health Alumni Panel

The Public Health Student Leadership Association presents “Leadership in Public Health”. This virtual panel will feature six Gillings School of Global Public Health alumni from diverse backgrounds. Join them for... Read more »

Joint Nonlinear Association and Prediction of Multi-view Data

Dr. Sandra Safo's research interests are in developing statistical methods and computational tools to help identify risk factors for complex diseases: multivariate statistical methods, statistical learning (including classification, discriminant analysis, association studies), data integration and feature selection methods for high dimensional data; currently integrative analysis of genomics, transcriptomics and metabolomics to help elucidate the complex... Read more »

Odum Institute Short Course — Logistic Regression

This online short course provides an introduction to logistic regression. Model specification, identification, estimation, hypothesis-testing, and interpretation of results are covered. Software to estimate these models is discussed, but not... Read more »

Data-Adaptive Regression Modeling in High Dimensions

Dr. Ashley Petersen's research focuses on developing methods in the area of statistical learning, and in building flexible and interpretable data-adaptive models that are useful in modern settings with large numbers of covariates. She develops methods for the analysis of calcium imaging data. As a member of the Biostatistics and Bioinformatics Core of the Masonic... Read more »

Spring PHield Trip to RTI

Explore public health in action! Join the virtual PHield trip with RTI International on Feb. 18 from 1-3:00 p.m. The event will feature a keynote on mobilizing global public health... Read more »

Statistical methods for single-cell and spatial RNA-seq

Dr. Christina Kendziorski's research concerns statistical methods and software for computational biology and genomics. Her group develops statistical methods and software for the analysis of data from high-throughput genomics experiments... Read more »

Greenberg Lectures: Scalable Statistical Inference of Large-Scale Whole Genome Sequencing Studies

The 2021 Bernard G. Greenberg Distinguished Lecture Series Featuring Professor Xihong Lin, Harvard University Lecture 1: May 20, 10-11:00 a.m. Scalable Statistical Inference of Large-Scale Whole Genome Sequencing Studies Big data from genome, exposome and phenome are becoming available at a rapidly increasing rate. Examples include Whole Genome Sequencing data, smartphone data, wearable devices, and... Read more »