Associate Professor of Statistical and Population Genetics
I received my PhD in computer science from Columbia University in 2014. I then spent three and a half years working on statistical and population genetics as a postdoctoral fellow at the Harvard Chan School of Public Health, and at the Broad Institute of MIT and Harvard. Prior to that, I obtained a bachelor’s and a master’s degree from Rome’s Sapienza University, and a master’s degree from Columbia University, all in computer science with a focus on artificial intelligence, machine learning, and cognitive robotics.
My research is at the intersection of statistics, computer science, and genetics. I develop methods to enable new types of analyses in statistical and population genetics, with a particular interest in problems that involve modeling and inference in large datasets. Specific areas of research include studying evolutionary parameters in the human genome (natural selection, mutation/recombination rates), reconstructing past demographic events using genetic data (migration, expansion/contraction of populations), studying the heritability and genetic architecture of complex traits (nature vs nurture), and detecting disease-causing variation in the human genome.
- Palamara, J. Terhorst, Y. Song, A. Price. High-throughput inference of pairwise coalescence times identifies signals of selection and enriched disease heritability. Nature Genetics, 2018.
- Palamara. ARGON: fast, whole-genome simulation of the discrete time Wright-Fisher process. Bioinformatics, 2016.
- Palamara, et al.. Leveraging distant relatedness to quantify human mutation and gene conversion rates. The American Journal of Human Genetics, 2015.
- Genome of the Netherlands Consortium. Whole-genome sequence variation, population structure and demographic history of the Dutch population. Nature Genetics, 2014.
- Palamara, T. Lencz, A. Darvasi, I. Pe’er. Length distributions of identity by descent reveal fine-scale demographic history. The American Journal of Human Genetics. 2012.