Network Reconstruction in Evolutionary Biology

A central challenge in biology and artificial intelligence is learning latent structure from data. In phylogenetics, this means reconstructing evolutionary histories from genetic data, often moving beyond trees to networks that capture hybridization and gene flow.

This talk focuses on identifiability: whether the underlying structure can be uniquely recovered from observed data. Computational algebraic geometry provides tools for understanding when network features can be recovered and when they cannot, even with unlimited data.