His basic research is focused on:
(i) using forward and reverse genetics to understand how bacteria evolve from environmental organisms into specialised lung pathogens and how they interact with the innate immune system;
(ii) developing new machine learning (ML) methods to gain systems-level understanding of bacterial pathobiology; and
(iii) creating new antibiotics by combining fragment-based, structure-guided methods with machine learning models to predict bacterial chemical permeability and metabolism.
Hi clinical research is focused on developing AI-driven home monitoring platforms to predict clinical deteriorations in Cystic Fibrosis (CF) and non-CF bronchiectasis (NCFB), understanding the genetic factors that predispose to nontuberculous mycobacterial infections and bronchiectasis, and creating methods to predict antibiotic susceptibility using bacterial genomics.