I am generally interested in understanding the relationship between biological sequence, structure and function. Specifically, I study how the primary sequence of an antibody determines its structure and function, how changes in genetic information affect the viral phenotype by developing deep learning models and conducting biological experiments

Antibody response


  • Assembled a dataset of ∼8,000 published antibodies to SARS-CoV-2 S from >200 donors.
  • Antibodies to RBD, NTD, and S2 have distinct convergent sequence and molecular features.
  • Public antibody clonotypes show recurring affinity maturation pathway.
  • Provided a proof of concept for antibody specificity prediction using deep learning.

The evolution of influenza virus


  • High-throughput combinatorial mutational scanning on Influenza virus neuraminidase (NA) antigenic region.
  • The local fitness landscape correlates well among strains and the pairwise epistasis is highly conserved.
  • Local net charge governs the pairwise epistasis in this antigenic region and residue coevolution is correlated with the pairwise epistasis between charge states.
  • Quantifying epistasis and biophysical constraint helps to build a model of influenza evolution.

Deep learning for antibody design


  • Deep learning model for antibody structure prediction.
  • Deep learning model for antibody affinity prediction.
  • Deep learning model for antibody specificity prediction.