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 »

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 »

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 and have considerable expertise in the experimental design and analysis of bulk RNA-seq studies and in single-cell RNA-seq. The group also uses high-throughput data from... Read more »

Statistical analysis of spatial expression pattern for spatially resolved transcriptomic studies

Dr. Xiang Zhou is a John G. Searle Assistant Professor of biostatistics who received his M.S. in statistics and PhD in neurobiology from Duke University (2010). His research focuses on developing statistical methods and computational tools for genetic and genomic studies. These studies often involve large-scale and high-dimensional data; examples include genome-wide association studies and... Read more »

Identifying effector genes of human GWAS variants by INFIMA

Dr. Sunduz Keles will give a biostatistics talk titled "Identifying effector genes of human GWAS variants by INFIMA". The Keles Research Group is interested in statistical and computational genomics and develops statistical and computational data science methods for problems in high-dimensional genomic and biomedical data. The group will exploit, leverage and integrate high-throughput functional, genomic,... Read more »