The Engelhardt Group is involved in developing innovative statistical models and methods in order to elucidate biological mechanisms of complex phenotypes and disease. Measurements of biological systems have both noise and systematic bias, and often the analytical goal is to identify low-dimensional substructure within a high-dimensional space. These qualities are well-addressed by model-based analyses. But the high dimension and scale of biological data makes parameter estimation in sophisticated models challenging. We address these challenges by developing hierarchical statistical models and approximate parameter estimation methods to gain access to interesting biological phenomena.
About
The Engelhardt Group is involved in developing innovative statistical models and methods in order to elucidate biological mechanisms of complex phenotypes and disease. Measurements of biological systems have both noise and systematic bias, and often the analytical goal is to identify low-dimensional substructure within a high-dimensional space. These qualities are well-addressed by model-based analyses. But the high dimension and scale of biological data makes parameter estimation in sophisticated models challenging. We address these challenges by developing hierarchical statistical models and approximate parameter estimation methods to gain access to interesting biological phenomena.
Statistical Analysis of Genetic Association Studies
Understanding how eQTLs work
Sparse latent factor models
Research Areas
News & Media
Not What but Why: Machine Learning for Understanding Genomics | TEDxBoston
Machine Learning to Understand and Prevent Disease
Barbara Engelhardt on How to Improve Statistical Analyses of Genomes
