About Me

I am a statistician deeply invested in solving data puzzles at the intersection of genomics, computational biology, and neuroscience. My work focuses on building robust statistical frameworks and generative models that help us understand the complex cellular architecture of the brain. I apply these high-level statistical tools to investigate the root causes of autism and other complex neuropsychiatric traits.

Modern technologies like single-cell RNA sequencing (scRNA-seq), proteomics, spatial transcriptomics and multiomics allow us to examine individual cells at unprecedented resolution. However, these cutting-edge techniques introduce massive, complex datasets filled with noise, missing data, and technical biases. That is where my research comes in. I develop the mathematical and statistical foundations needed to separate biological signals from experimental noise, ensuring that our discoveries in genomics are both mathematically sound and biologically meaningful. My areas of specialty include high dimensional inference, deep learning, causal inference and network analysis.

contact

UPMC Professor of Statistics and Life Sciences
Department of Statistics and Computational Biology
Carnegie Mellon University
Baker Hall 132F
Pittsburgh, PA 15213

Email: roeder at andrew.cmu.edu