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Fast statistical methods for big data genetics

Date and Time
Tuesday, April 25, 2017 - 12:30pm to 1:30pm
Computer Science Small Auditorium (Room 105)
CS Department Colloquium Series
Prof. Mona Singh

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As genomic data sizes have grown exponentially over the past decade, efficient statistical analysis has become a key challenge in quantitative genetics. I will describe three ongoing research thrusts in the fields of mixed model analysis, haplotype phasing and imputation, and somatic structural variant detection, each taking place at the intersection of mathematics, computer science, and genetics.

I am currently a Postdoctoral Research Associate in Statistical Genetics at the Harvard T.H. Chan School of Public Health working with Dr. Alkes Price. The broad aim of my research is to develop efficient computational algorithms to analyze very large genetic data sets. My current areas of focus include (1) fast algorithms for genotype phasing and imputation, (2) linear mixed model methods for heritability analysis, association testing, and risk prediction, and (3) computational phase-based detection of pre-cancerous mosaic chromosomal aberrations. Several of my research projects have resulted in software packages now in use by the genetics community.

Prior to joining the Harvard Chan School in 2013,  I received my Ph.D. in Applied Mathematics from the Massachusetts Institute of Technology and my B.S. in Mathematics from the California Institute of Technology.

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