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Bioinformatician

Roel Ophoff Lab, UCLA Psychiatry and Human Genetics

Project: Meta-analysis of epigenetic aging in bipolar disorder.

• Performed large-scale bioinformatics analysis of genome-wide DNA methylation profiles from bipolar disorder cohorts, leveraging advanced DNA methylation clocks to dissect associations between epigenetic aging and clinical phenotypes, such as chronological age, sex, illness duration.
• Utilized high-performance computing clusters (hoffman2) to execute parallelized preprocessing workflows, including quality control, normalization, and feature extraction of methylation data.
• Developed and optimized statistical models in R using packages such as dnaMethyAge, minfi, limma, integrating machine learning techniques including regularized regression (Lasso, Ridge), random forests, and gradient boosting.

Research Assistant

Chao Peng Lab, UCLA Neurology

Project: Uncover XX vs. XY differences in AD and PD pathology using Four Core Genotypes (FCG) mouse model.

• Design and conduct experiments on tau and mouse α-Syn preformed fibrils (PFFs) preparation.
• Stereotactic injection of Amyloid-β and α-syn PFF in 5XFAD mice; mouse husbandry and colony management; in vivo miniscope calcium imaging; spatial memory behavioral testing.

Research Assistant

Soojin Yi Lab, UCSB EEMB

Project: Connecting Epigenome to Health in Marine Organisms.

• Conducted DNA methylation studies on marine organisms to develop DNA methylation clocks.
• Processed DNA extracted from mussel and starfish tissues for Reduced Representation Bisulfite Sequencing (RRBS), optimizing bioinformatics pipelines.
• Developed Python-based tools for automated shell ring counting and standardized workflows for 3D shell scanning and DNA extraction.

Research Assistant

Susan Mazer Lab, UCSB EEMB

Project: Computational Analysis of Style Length Impact on Pollination Efficacy and Reproductive Fitness in Nemophila menziesii.

• Investigated the relationship between style length and pollination efficacy, testing its correlation with lifetime fecundity using linear regression, ANOVA, and mixed-effects models in R.
• Digitized pollen, seed count, and flower size data; implemented interactive data visualization dashboards in R Shiny.
• Developed automated data preprocessing pipelines in R to identify phenotypic patterns from microscopy observations.

Master of Science

University of California, Los Angeles

Data Science

• Introduction to Data Science
• Principles of Biostatistics
• Machine Learning

Bachelor of Science

University of California, Santa Barbara

Biological Sciences