AI & Computational Methods

Our group develops AI and computational approaches that turn complex genomics data into testable biological insight. We focus on methods that are both high-performing and interpretable, so researchers can use model outputs to understand mechanisms rather than only generate scores. This includes machine learning for gene and variant prioritization, network-aware modeling of molecular systems, and statistically grounded frameworks for noisy, high-dimensional biomedical data.

At the systems level, we build models for pathway activity inference, multi-omics integration, and single-cell analysis to connect signals across molecular layers. We combine representation learning, probabilistic inference, and causal reasoning to improve robustness and biological relevance across diverse cohorts and experimental settings. These methods are designed to support end-to-end discovery workflows, from data interpretation to target nomination and experimental follow-up.

We prioritize open, reusable software so these methods are accessible beyond our lab. Representative tools include AI-MARRVEL, NMRQNet, SPA-STOCSY, and CRISPRcloud. Together, these resources support computational biologists, experimental teams, and translational collaborators working on real-world genomic and disease-focused questions.