Disease is rarely a static state. It emerges through coordinated molecular changes that unfold across time, tissues, cell types, and regulatory networks. Understanding those changes requires models that can represent biological systems as dynamic landscapes rather than isolated lists of altered genes or metabolites.
My current research direction focuses on building computational frameworks that map high-dimensional molecular measurements into interpretable latent spaces. These spaces can help describe how disease trajectories diverge from normal physiology, how perturbations propagate through regulatory programs, and where targeted interventions may shift an unstable biological state toward a more controlled or healthy configuration.
This is the direction I refer to as reprogrammable medicine: using computational models to identify actionable molecular states, predict intervention points, and support therapies that guide biological systems rather than only reacting to end-stage phenotypes.