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 »

National Public Health Week: Building Bridges to Better Health

During the first full week of April each year, APHA brings together communities across the United States to observe National Public Health Week as a time to recognize the contributions of public health and highlight issues that are important to improving our nation. APHA creates new NPHW materials each year that can be used during... Read more »

Towards a unified methodology of study design and statistical analysis for causal inference in implementation science

Dr. Donna Spiegelman has a joint doctorate in biostatistics and epidemiology and her research is motivated by problems that arise in epidemiology and require biostatistical settlement; in part a focus on troubleshooting methods for study design and data analysis to reduce bias in estimation and inference due to measurement error or misclassification in the exposure... 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 »

Greenberg Lectures: Large-Scale Hypothesis Testing for Causal Mediation Effects with Applications in Genome-wide Epigenetic Studies

The 2021 Bernard G. Greenberg Distinguished Lecture Series Featuring Professor Xihong Lin, Harvard University Lecture 2: May 20, 3-4 p.m. Large-Scale Hypothesis Testing for Causal Mediation Effects with Applications in Genome-wide Epigenetic Studies In genome-wide epigenetic studies, it is of great scientific interest to assess whether the effect of exposure on a clinical outcome is... Read more »

Greenberg Lectures: Regression Models for COVID-19 Epidemic Dynamics with Incomplete Data

The 2021 Bernard G. Greenberg Distinguished Lecture Series Featuring Professor Xihong Lin, Harvard University Lecture 3: May 21, 10-11 a.m. Regression Models for COVID-19 Epidemic Dynamics with Incomplete Data Modeling infectious disease dynamics has been critical throughout the COVID-19 pandemic. Of particular interest are the incidence, total prevalence, and effective reproductive number (Rt). Estimating these... Read more »

Gillings Virtual Reception in Conjunction with JSM 2021

Join the Department of Biostatistics for a Gillings Virtual Reception in conjunction with the Joint Statistical Meetings (JSM) 2021. To join this virtual event, you must register in advance. After registering, you will receive a confirmation email containing information about joining the reception with a personal URL for your use to join the event. Contact... Read more »