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 »