Research Experience

Bioinformatics Research Analyst
August 2024 – Present

My current research focuses on developing computational methods for non-invasive detection and monitoring of brain tumors using cell-free DNA (cfDNA) obtained from plasma samples.

Working closely with clinicians, surgeons, and translational researchers, I develop and maintain analytical pipelines that integrate fragmentomics, methylation profiling, and sequencing-based biomarkers to improve liquid biopsy performance.

• Developing and maintaining ARTEMIS-DELFI workflows for cfDNA fragmentomics analysis.
• Processing Illumina whole-genome sequencing, Oxford Nanopore sequencing, ATAC-seq, and EM-seq datasets.
• Evaluating the impact of focused ultrasound (FUS)-mediated blood-brain barrier disruption on cfDNA release into circulation.
• Integrating clinical variables including Ki67, tumor mutational burden, tumor size, and treatment response into downstream analyses.
• Generating publication-quality figures, statistical analyses, and research reports supporting grants and manuscripts.

Senior Data Associate
March 2021 – May 2024

At Sound Agriculture, I worked on large-scale genomics and epigenomics projects focused on improving crop performance and understanding molecular mechanisms underlying agriculturally important traits.

• Processed and analyzed large whole-genome sequencing datasets from diverse plant species.
• Generated custom genomes using NanoCaller, bcftools, and cloud-based workflows.
• Designed AWS Batch pipelines capable of processing more than 50 TB of sequencing data.
• Co-developed sounDMR, an R package for rapid methylation analysis of Oxford Nanopore sequencing data.
• Developed an internal Python-based oligonucleotide design platform used by experimental teams.
• Collaborated with molecular biologists and external partners to translate sequencing results into actionable biological insights.

Staff Research Associate II / Bioinformatics Analyst
June 2020 – March 2021

As the bioinformatics lead on a translational rheumatology project, I investigated molecular signatures associated with progression from undifferentiated arthritis to rheumatoid arthritis.

• Built RNA-seq analysis workflows from raw sequencing reads through differential expression analysis.
• Utilized HTStream, Kallisto, MultiQC, and R/Bioconductor for preprocessing and downstream analysis.
• Performed longitudinal analyses to identify biomarkers associated with disease progression.
• Conducted pathway enrichment, functional analysis, and network-based investigations.
• Contributed to an abstract accepted at the American College of Rheumatology (ACR).

Research Associate Intern
May 2019 – December 2019

My work focused on computational analysis of transcriptomic datasets and development of machine learning approaches for disease classification.

• Performed single-cell RNA sequencing analyses using Seurat.
• Analyzed large public datasets including TCGA and GTEx resources.
• Developed machine learning models for glioblastoma classification.
• Conducted differential expression, network analysis, and WGCNA studies.
• Applied dimensionality reduction and visualization approaches including PCA.

• Cell-Free DNA Fragmentomics
• Liquid Biopsy Development
• Cancer Genomics
• Brain Tumor Biomarkers
• Epigenomics and Methylation Analysis
• Oxford Nanopore Sequencing
• RNA Sequencing
• Cloud-Based Genomics Pipelines
• Machine Learning in Biomedical Research