Microbiologist🪴Computational Biologist bridging AI & biology | Rare-cell detection, scRNA-seq, and interactive genomics tools 🚀👾

I’m a researcher at UC Riverside’s MIGCrest Lab, passionate about leveraging machine learning and bioinformatics to decode complex biological systems. My current work focuses on single-cell transcriptomics, neural crest development, and building reproducible tools that make genomic data more accessible and interpretable.

• Rare-cell population detection in single-cell RNA-seq (scRNA-seq) using CIARA and BigSur
• Building interactive Shiny apps for exploring gene expression in early development
• Developing reproducible workflows in R and Python (Seurat, Scanpy, Snakemake, Nextflow)
• Researching neural crest lineage segregation during early embryogenesis

Languages & Environments:

Bioinformatics & ML:

Visualization & Tools:

R pipeline for identifying rare cell populations (neural crest stem cells, immune subsets) using BigSur.
Outcome: End-to-end reproducible workflow with Conda environment management and publication-ready visuals.
Tech: R, BigSur, Seurat, Conda, UMAP, QC plots

Automated quantification of PTBP1 (RRM2 domain) localization from heterokaryon assays using ImageJ/Fiji and Python.
Outcome: Analyzed 26,040 cells; identified novel nuclear retention pattern; presented initial results for this project at CSUF Research Symposium (2021).
Tech: ImageJ, Python, OpenCV, scikit-image, Dash

Benchmarking CIARA vs BigSur on human gastrula datasets (GSE136447, E-MTAB-9388).
Outcome: CIARA achieved recall = 0.92 at low read depth; identified rare hemogenic endothelial progenitors.
Tech: R, Seurat, Python, Jupyter, UMAP, Volcano Plots

Interactive Shiny app for exploring Seurat clusters, gene expression, and marker heatmaps in neural crest datasets.
Outcome: Reduced data exploration time by ~40% for wet-lab collaborators.
Tech: R, Shiny, Seurat, ggplot2, Plotly, VennDiagram

(2023 – Present)

• Built scRNA-seq pipelines (Seurat, CIARA, BigSur) for rare population detection
• Developed a ShinyApp used by the lab for exploratory data analysis
• Integrated multi-omics data (Visium spatial transcriptomics) with transcriptomic results

(May – Jun 2024)

• Applied ML models (XGBoost, Random Forest, Logistic Regression) to clinical + omics datasets
• Delivered reproducible workflows in Python with scikit-learn

• B.S. Microbiology @ University of California, Riverside (2023 – 2025)
• Machine Learning for Medicine Track @ SCIP - Science Coding Immersion Program (2024)
• Biotechnology Laboratory Assistant Certificate @ Fullerton College (2020)

• Open science and reproducible workflows
• Single-cell + spatial multi-omics integration
• Rare-cell algorithm development
• Ethical AI in biomedical research
• Mentoring students in computational biology

👾 Explore my repositories below! ⤵