Bioinformatics Graduate Student

I am a bioinformatics graduate student with hands-on experience in scRNA-seq, RNA-seq, spatial transcriptomics, and scATAC-seq analysis. I have worked on end-to-end NGS workflows that include quality control, read alignment, quantification, differential expression, batch-effect correction, and cell-type annotation using R and Python.

I have developed multi-omics integration approaches to align tens of thousands of cells across platforms and improve cross-modal correspondence. My work includes using tools such as Seurat, DESeq2, STAR, FASTQC, Scanpy, and optimal transport-based methods to support reproducible genomic analysis pipelines in Linux-based environments.

In my current research roles, I collaborate with wet-lab and translational R&D teams to process high-dimensional spatial and single-cell datasets. I also optimize computational workflows through parameter tuning and parallelization, and I evaluate alignment performance using statistical and machine learning metrics.

Alongside research, I have experience as a teaching assistant, where I support students in applied programming for biomedical data analysis. I help with Python programming, data analysis, bioinformatics lab exercises, grading, and academic support.

My technical background spans programming, bioinformatics, database work, pathway analysis, and data visualization. I have also completed projects in survival analysis, biomarker classification, gene expression analysis, algorithm comparison, and database design, which strengthened my analytical and problem-solving skills.

I am motivated by research-driven work at the intersection of biology, computation, and data science. I enjoy building scalable analysis pipelines, interpreting complex biological data, and contributing to projects that advance biomedical discovery.