Alignment-aware single-cell clustering analysis
Extending clustering alignment ideas to the single-cell analysis pipeline to compare models, identify cluster-informative genes, and stabilize downstream interpretation.
Projects
ML-based methods for multivariate genetic association analysis
Designed ML-MAGES, a machine-learning framework for shrinkage, pattern discovery, and gene-level aggregation in high-dimensional genetic association studies.
Clustering alignment for population structure analysis
Developed methods and tools for aligning latent ancestries across repeated population structure analyses so results remain interpretable across runs and choices of K.
Mathematical properties of allele-sharing dissimilarities
Analyzing within- and between-population dissimilarity measures as functions of allele-frequency distributions to clarify when and how these statistics should be used.
Cultural evolution through population-genetic methods
Adapts ideas from genetic evolution to study how cultural traits, social learning, and inherited conventions vary across groups and over time.