Research Projects

Computational biology and bioinformatics research focused on tuberculosis susceptibility, immunogenetics, and population genomics at UC Davis

TB Epidemiological Risk Assessment

Published

Built case-control assignment algorithm utilizing clinical, behavioral, and self-report data from over 50 variables. Implemented Random Forest and Logistic Regression models to predict active tuberculosis progression in 1000+ patients from rural South African clinics.

PythonRandom ForestLogistic RegressionClinical DataEpidemiology

TB scRNA-seq Immunogenetic Analysis

Active

Leading the first-ever single-cell RNA sequencing analysis of a TB case-control cohort using 10X Genomics. Profiling gene expression and cell surface protein markers in PBMCs to identify novel genetic variants and immune mechanisms driving TB progression.

scRNA-seq10X GenomicsPythonRImmunogeneticsSingle Cell

CAAPA Pathogenic Variant Annotation

Active

Applying variant effect prediction algorithms to a global genetic dataset of 3000+ individuals from 60 populations. Created consensus ML metric for classifying deleterious genetic variants and identified correlations with evolutionary conservation tools.

PythonMachine LearningVariant AnalysisPopulation GeneticsBioinformatics

Research Impact & Goals

My research directly contributes to understanding tuberculosis susceptibility in genetically diverse populations. By developing computational methods and analyzing multiomic datasets, I aim to identify biomarkers that could lead to earlier detection and more personalized treatment approaches. This work has implications not only for TB management but also for advancing precision medicine approaches in underrepresented populations globally.