Statistical genetics and metascience

We are building our new lab at Carnegie Mellon University in January 2025.

We are a research group lead by Dr. Richard Border, a new assistant professor in the Department of Computational Biology at the Carnegie Mellon University School of Computer Science. We develop scalable methods for understanding the genetic basis of complex disease with an emphasis on separating genetic confounds and other structural artifacts from biologically-relevant genetic signal. We also think about ways to improve the robustness of the statistical methods accelerate the process of scientific discovery in the world of statistical genetics.

Some of the topics we’re working on include:

• Using stochastic algorithms and alternative data structures to accelerate statistical inference at biobank scale
• Identifying genetic variation underlying disease risk in the presence of assortative mating and sources of population structure
• Algorithms and software for scalable / flexible simulation of genotype / phenotype data under non-standard models of genetic architecture and transmission dynamics
• Reproducible comprehensive benchmarking and sensitivity analysis of statistical genetic methods
• Scale-invariant quantification of non-additive contributions of genetic and non-genetic factors
• Probalistic programming languages for scalable Bayesian inference in a distributed memory context
• Developing novel statistical and statistical learning approaches to understanding the genetic basis of psychiatric disease

You can contact Dr. Richard Border via email at rborder@cs.cmu.edu.