Kathryn Roeder
Carnegie Mellon University
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Primary Section: 32, Applied Mathematical Sciences Secondary Section: 41, Medical Genetics, Hematology, and Oncology Membership Type:
Member
(elected 2019)
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Biosketch
Kathryn Roeder is the UPMC Professor of Statistics and Life Sciences in the Departments of Statistics and Data Science and Computational Biology. Dr. Roeder has developed statistical and machine learning methods in a wide spectrum of areas, including high dimensional data problems in genetics and genomics. Her work focuses on statistical methods to reveal the genetic basis of complex disease. She is one of the leaders of the Autism Sequencing Consortium, an international organization dedicated to discovering the genetic etiology of autism. She received the Committee of Presidents of Statistical Societies (COPSS) Presidents’ Award (1997), Snedecor Award for outstanding work in statistical applications (1997) and Distinguished Achievement Award and Lectureship (2020), three of the most prestigious awards in her field. In 2013, she received the Janet L. Norwood Award for outstanding achievement by a woman in statistical sciences. She is an elected fellow of the American Statistical Association, the Institute of Mathematical Statistics and AAAS. In 2019 she was elected to the National Academy of Sciences.
Research Interests
I specialize in developing statistical methods for genetics, genomics and adapting modern statistical methods to the complexities of real data analysis. Recently I have been developing statistical methods for the analysis of single cell RNA-seq data, especially brain cells. This work includes developing methods for embedding cells optimally in a low dimensional representation, using data integration and transfer learning to combine data from disparate sources, representing cellular developmental in a tree structure, and estimating gene networks. I also develop methods for identifying genetic risk factors for psychiatric disorders, particularly autism. We are also developing methods for the analysis of single-cell CRISPR pooled screens to evaluate the impact of precisely targeted perturbations on the whole transcriptome.
