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 »

Synthesizing evidence about harms in systematic reviews

2301 McGavran-Greenberg Hall McGavran-Greenberg Hall, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States

Dr. Evan Mayo-Wilson is an epidemiologist with training and experience in intervention design, evaluation and translation of clinical evidence into policy and practice. His research focuses on evaluating the effectiveness of pharmacological and behavioral interventions; improving methods for clinical trials and systematic review; and developing methods and interventions to increase research tran11sparency and openness. View... Read more »