As part of the Lander Lab and Novo Nordisk Foundation Center (NNFC), tackled multiple projects with a general focus on better understanding the genome through cellular models, for example:* Analyzed perturb (CRIPSRi) and base-edited single-cell RNA and ATAC-seq datasets in IPSCs, hepatocytes, and neuronal cells using existing statistical methods as well as novel regressions for detecting changes in gene expression.* Implemented multiple analysis pipelines ranging from eQTL detection and… Show more As part of the Lander Lab and Novo Nordisk Foundation Center (NNFC), tackled multiple projects with a general focus on better understanding the genome through cellular models, for example:* Analyzed perturb (CRIPSRi) and base-edited single-cell RNA and ATAC-seq datasets in IPSCs, hepatocytes, and neuronal cells using existing statistical methods as well as novel regressions for detecting changes in gene expression.* Implemented multiple analysis pipelines ranging from eQTL detection and annotation to parallelized donor assignment in cellular village data.I have also led workshops, presented posters at multiple conferences (NNFC Symposium, Oct 2023, and Broad Retreat, Dec 2023), and served on a panel at EMBL-EBI's program "Best practice for perturb-seq data analysis and integration", alongside leading PIs in the field. Show less

The Meyer Lab at UCLA• Developed tensor decomposition method (in broader dimensionality reduction class) capable of handling longitudinal sample data. Master's thesis: "Continuous Tensor Factorization Effectively Reduces Data with Longitudinal Measurements" (https://escholarship.org/uc/item/45m3s58s).• Investigated tensor factorization approaches for the study of systems serology data collected from HIV and COVID-19 patients, with the goal of predicting protective immunity in patients… Show more The Meyer Lab at UCLA• Developed tensor decomposition method (in broader dimensionality reduction class) capable of handling longitudinal sample data. Master's thesis: "Continuous Tensor Factorization Effectively Reduces Data with Longitudinal Measurements" (https://escholarship.org/uc/item/45m3s58s).• Investigated tensor factorization approaches for the study of systems serology data collected from HIV and COVID-19 patients, with the goal of predicting protective immunity in patients to aid the development of vaccines. Research led to second authorship on paper: "Tensor‐structured decomposition improves systems serology analysis" doi: 10.15252/msb.202110243• Hired as a full-time undergraduate researcher for the Summer of 2020.Data analysis done in Python and Julia, and managed through GitHub. Show less

• Utilized machine learning techniques to investigate synthetic lethality interactions that indicate possible therapeutic targets in patients with cancer. • Implemented statistical methods for hypothesis testing; worked with DepMap data.

Peer tutor. Facilitate UCLA students in their understanding of class materials in various subjects. Main subjects taught include Differential and Integral Single Variable Calculus, Integration and Infinite Series, Multivariable Calculus, Differential Equations, and Genetics, Evolution, and Ecology.