🧬 Research Vision
Decoding Microbial Life with AI & Metagenomics
My research lies at the intersection of computational biology, machine learning, and microbiology. I develop and apply deep learning models to explore massive metagenomic datasets, with the goals of describing planetary viral diversity and elucidating functions of novel genes.
🦠 Virology
🏷️ Functional Annotation
🧬 Microbiome
■ Core Research Directions
Biological Sequence Analysis
- CRISPR-PAM: Metagenomic mapping of PAM preferences across millions of Cas9 proteins to expand targeting options.
- DeepFRI: GNN-based function prediction directly from sequence-derived contact maps at database scale.
- scikit-bio: Contributions to Python's core bioinformatic library for sequence analysis and diversity metrics.
Microbiome - Host Relationship
- Probiotics: Clinical trial mapping multi-species probiotics response on gut microbiota functions in obese postmenopausal women.
- Rhinosinusitis: Investigating spatial variability in chronic rhinosinusitis sinus microbiome, highlighting patient-specific differences.
Machine Learning
- Metagenomic DeepFRI: Integrating deep-learning GNNs into bioinformatics pipelines to annotate novel gut microbial genes.
- TM-Vec2: Lightweight sequence embedder trained via knowledge distillation to predict protein structure similarity (TM-scores) at scale.