Ph.D. Candidate in Bioinformatics at the University of Georgia
Computational virology · Phylodynamics · Structural biology
I develop phenotype-aware frameworks that connect viral genetic diversity with protein structure, receptor biology, evolutionary history, and host ecology. My current research focuses on highly pathogenic avian influenza H5N1/H5Nx, especially clade 2.3.4.4b, and on identifying population-level signals of cross-host emergence and pandemic risk.
- Population-scale H5N1 phenotype mapping: connecting large HA sequence datasets with time-resolved phylogenies, structural models, physicochemical traits, receptor-binding analyses, and energetic estimates.
- Structure-informed phylodynamics: integrating sequence evolution, host transitions, ecological context, and molecular phenotype.
- Influenza receptor biology: studying α2,3- and α2,6-linked sialic-acid recognition, receptor-binding-site remodeling, and glycan interactions.
- Predictive biology: developing proficiency in protein language models and machine-learning approaches for viral phenotype and risk prediction.
Python · R · Bash · Linux · SLURM · IQ-TREE · BEAST · TreeTime · Nextstrain · AlphaFold 3 · Rosetta · FoldX · Amber · GROMACS · PyMOL
- Standing HA phenotypic breadth shapes H5N1 cross-host potential — manuscript under review at Nature
- Dynamic Risk Maps Predict Highly Pathogenic Avian Influenza Hotspots Across North America — Research Square preprint
- N-glycosylation at the receptor binding site drives differences in receptor binding specificity between influenza B virus lineages — Journal of Virology Editor’s Pick
Website · Google Scholar · ORCID · LinkedIn