UPMC Professor of Statistics and Life Sciences
- BH 132F
5000 Forbes Avenue
Pittsburgh, PA 15213
Kathryn Roeder’s research focuses on developing statistical methods for analysis of genetic and genomic data with an aim to find associations between patterns of genetic variation and complex disease. To solve biologically relevant problems, her team utilizes modern statistical methods such as high dimensional statistics, statistical machine learning, nonparametric methods and networks. Her group developed tools for identifying autism risk genes from de novo mutations and, together with the Autism Sequencing Consortium, they have identified more than one hundred autism risk genes. Roeder’s team has developed some of the key statistical tools for the analysis of whole-genome sequencing data, and these methods have helped interpret the impact of noncoding variants on autism and other neuropsychiatric disorders. A recent focus of her group is developing tools for the analysis of single-cell multi-omic data.
Roeder is currently the UPMC Professor of Statistics and Life Sciences in the Departments of Statistics & Data Science and Computational Biology. She earned her Ph.D. in statistics at Pennsylvania State University, after which she was on the faculty at Yale University for the six years before coming to CMU in 1994. In 1997 she received the COPSS Presidents’ Award for the outstanding statistician under age 40 and the Snedecor Award for outstanding work in statistical applications. In 2020 she was awarded the COPSS Distinguished Achievement Award and Lectureship. She is an elected fellow of the American Statistical Association and the Institute of Mathematical Statistics. In 2019 she was inducted into the National Academy of Sciences.
Specific Research Interests
A primary goal of my research group is to develop statistical tools for finding associations between patterns of genetic variation and complex disease. To solve biologically relevant problems, we utilize modern statistical methods such as high dimensional statistics, statistical machine learning, nonparametric methods and networks. Data arises from primarily from Next Generation Sequencing and gene expression arrays. Our methodological work is motivated by our studies of schizophrenia, autism and other genetic disorders.