Next-generation sequencing platforms have completely revolutionized the way biology is studied and medicine is practiced. However, such platforms have various error rates and the types of errors generated are not always consistent across technologies. Different groups across the world are sequencing very large cohorts of individuals which raises questions about how to process, store and query such datasets. In addition, DNA extracted from fossils has enabled us to peak at the history of various species including our own. Despite its potential, this data suffers from high error rates, contamination from different sources and heavy DNA fragmentation. 

We are primarily interested in developing robust computational tools using primarily Bayesian and maximum-likelihood methods to address the aforementioned issues.  

We generally aim at understanding how species have changed over time. However, computational methods that account for uncertainty in the data still need to be developed.