While the amount of genetic data is ever-increasing, many current methodologies to explore such data are based on heavy computation that does not scale well. This is particularly true in Phylodynamics, a field at the crossing of epidemics and phylogenetics, in which the viral genetic data (eg HIV, Ebola virus, HCV, Flu virus, Coronavirus) from sampled patients is used to estimate the speed and the mechanisms of disease spread in population.
Jakub’s thesis aims at developing new methods based on machine learning in phylodynamics and evolution to contribute in bridging the gap between the data abundance and the limited application of current methods. His project is supervised by Dr Olivier Gascuel.
