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PhD

Duke University

Genetics and Genomics

BA

Brown University

Biology

Graduate Researcher

Alejandro Ochoa Lab

• Project: Leveraging ancient DNA in studying human adaptation to vivax malaria in Africa in admixture context.
• Developing a novel, regression-based single-nucleotide imputation approach that improves imputation accuracy on ancient African DNA which are challenging due to (1) low coverage of ancient DNA sequences and (2) low linkage disequilibrium among African populations.
• Benchmarked performance of the low-coverage imputation tool GLIMPSE2 in the imputation of ancient African genomes using simulations (SLiM, Msprime, Tskit).
• Built and performed pipelines for nucleotide imputation of the Duffy antigen receptor locus with custom African reference panel using Minimac4 and Beagle5.4 on ancient African genomes. Performance evaluation of above imputations was done via leave-one-out cross-validation.
• Characterized population structures in ancient and modern African human sequencing data using Principal Component Analyses (PCA), \(F_("ST")\), \(F\)-statistics, ADMIXTURE software and the popkin R package.

Rotation Graduate Researcher

Amy Goldberg Lab

• Developed a pipeline for joint analyses of archaeological (radiocarbon dating) and genetic data (whole genome sequences) in studying the Neolithic Revolution —a wide-scale transition into agricultural society promoting population growth —in East Asian populations.

Rotation Graduate Researcher

Craig Lowe Lab

• Designed and programmed a genome simulation algorithm as part of the novel genomic toolkit (Gonomics) using Golang.
• The program simulates genetic sequences from populations under the Wright-Fisher model with adjustable (1) population size, (2) number of generations, (3) mutation rate & (4) selection coefficient. The simulation extends beyond the Wright-Fisher model’s infinite-site assumption, allowing back mutation and multiallelic polymorphism in the simulated sequences.

Rotation Graduate Researcher

Jenny Tung Lab

• Built a linear mixed-effect regression model for studying the effects of energy expenditure (3-dimensional motionmeasured by accelerometers) on gene expression (bulk RNAseq data) in Kalahari meerkats. The model included random effects from kinship coefficients from known pedigrees.

Rotation Graduate Researcher

Mohamed Noor Lab

• Implemented the GATK best practice pipeline to prepare and analyze sequencing data generated from Drosophila melanogaster.