Selected work

Projects.

Selected software, data, and analytics projects spanning translational biology and real-world healthcare data.

Cloud infrastructure · Proteomics

Cloud-Based DIA Proteomics Pipeline

Designed and validated a reproducible AWS cloud pipeline for automated Data-Independent Acquisition (DIA) proteomics quantification. The pipeline moves raw mass-spectrometry data through a secure S3-to-EC2/Batch workflow, orchestrates multi-stage processing with Nextflow, and runs DIA-NN in containerized environments - transforming instrument RAW files into analysis-ready protein/precursor matrices, QC visualizations, and reproducible HTML reports, with built-in resumability, provenance tracking, and checksummed archival.

Cloud EngineeringAWSNextflowDockerDIA-NNProteomics View project on GitHub

Read on Medium

Distributed imaging pipeline · Spatial transcriptomics

EASI-FISH Distributed Spatial Imaging Pipeline

Played a significant role in the development, optimization, and deployment of a Nextflow DSL2 pipeline for large-scale EASI-FISH microscopy processing and downstream spatial transcriptomic analysis. The workflow orchestrates block-distributed multi-round deformable image registration, N5/Zarr-based volume stitching and fusion, 3D cell segmentation, and subpixel transcript-spot detection, enabling reproducible processing of multi-terabyte imaging datasets across workstation and HPC (LSF/Slurm) environments using fully containerized execution.

Nextflow DSL2HPC (LSF/Slurm)Apache SparkN5/ZarrBigStream RegistrationDeformable Image Registration3D Cell SegmentationRS-FISH Spot DetectionSpatial TranscriptomicsDocker/Singularity View project on GitHub

Multimodal Omics Integration

Integrating molecular datasets to uncover biological mechanisms, identify biomarkers, and prioritize therapeutic targets.

Project details coming soon →

Deep Learning for Drug Repurposing

Predictive models that transform high-dimensional biological data into testable hypotheses for precision medicine.

Project details coming soon →
Single-cell genomics · Big Data Analysis

End-to-End Single-Cell Analysis of Inter-Cellular Crosstalk & Signaling

Conducted end-to-end single-cell RNA-seq analysis investigating how subventricular zone (SVZ) microglia and neural stem/progenitor cells (NSPCs) interact to shape the neurogenic response following ischemic stroke. The pipeline spans raw count QC, SCTransform normalization, PCA/UMAP dimensionality reduction, and cell-type annotation, followed by applied CellPhoneDB permutation-based ligand–receptor inference, integrating cell type–specific mean gene expression, receptor-complex stoichiometry, and empirical P-values derived from 1,000-iteration label permutation testing to statistically rank significant intercellular signaling interactions between microglial subpopulations and NSPC states.

scRNA-seqSCTransformUMAP/PCACellPhoneDBLigand-Receptor InferencePermutation TestingCell-Cell CommunicationSeurat/Scanpy View project on GitHub