Personal Biography

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.

Research and Teaching

My research is at the intersection of statistics, computer science, and genetics. I develop new statistical and machine learning algorithms to enable new types of analyses in large human genomic data sets. Specific areas of research include modeling and inferring genealogical relationships in large data sets (identity-by-descent, ancestral recombination graph); reconstructing past demographic events using genetic data (migration, expansion/contraction of populations); studying evolutionary parameters in the human genome (natural selection, mutation/recombination rates); studying the heritability and genetic architecture of complex traits (nature vs nurture); detecting disease-causing variation in the human genome (association).

I teach students in maths, covering first and second year probability, statistics, and algebra, and a course in statistical machine learning at the Department of Statistics and other doctoral training centers.

Representative Publications

  • Lazaridis I. et al. (2025) The genetic origin of the Indo-Europeans. Nature.
  • Loya H., Kalantzis G., Cooper F., Palamara P.F. (2025) A scalable variational inference approach for increased mixed-model association power. Nature Genetics.
  • Stricker T., Zhang B.C., Cheng J.Y., Gazal S., Dendrou C.A., Nahkuri S., Palamara P.F. (2024) Genome-wide classification of epigenetic activity reveals regions of enriched heritability in immune-related traits. Cell Genomics.
  • Zhang B.C., Biddanda A., Gunnarsson Á.F., Cooper F., Palamara P.F. (2023) Biobank-scale inference of ancestral recombination graphs enables genealogical analysis of complex traits. Nature Genetics.
  • Palamara P.F., Terhorst J., Song Y.S., Price A.L. (2018) High-throughput inference of pairwise coalescence times identifies signals of selection and enriched disease heritability. Nature Genetics.