Created generative artificial intelligence (AI) agents using retrieval-augmented generation (RAG) techniques and integrated them into existing user interfaces to enhance scientific productivity, in addition to developing a user-friendly API to efficiently manage agents and collections.Demonstrated commercial potential of T cell and B cell receptor (TCR/BCR) immune repertoire sequencing data through in-depth exploratory analysis of individual features and entropy metrics by identifying correlations with survival outcomes among oncology patients.Engineered scalable software, reproducible reports, and interactive applications that enabled scientific inquiry for internal teams and customers using a complex BigQuery data model, significantly reducing time to insights for biomarker discovery and validation studies.

Initiated the development of deep learning models to improve molecular diagnosis and subtype classification of tuberculosis from gene expression data.Authored a technical proposal on implementing federated machine learning for members of the RePORT Consortium to accelerate collaborative tuberculosis research while preserving data privacy.

Developed the MultimodalExperiment data structure for integrated management of bulk and single-cell sequencing data and implemented relational database-like methods to create an exceptionally fast and storage-efficient representation of multimodal multiomics data.Led longitudinal metabolomics analysis of PD-1/PD-L1 Immuno-Oncology (IO) therapy response from the processing of clinical samples through the presentation of results to oncology collaborators and established metabolites associated with progression-free and overall survival outcomes.Created a data resource with more than 10,000 clean, high-quality transcriptomics samples from human hosts infected with tuberculosis to enable machine learning analyses by processing raw data from microarray and sequencing studies through a standardized pipeline (Docker/R/shell/Nextflow).

Published multiomics analysis describing tumor evolution in high-grade serous ovarian cancer with a diverse group of scientific collaborators and resolved why transcriptomic subtypes are not appropriate targets for precision medicine using bulk and single-cell RNA sequencing data.Released curated data from the cBioPortal and TCGA compendia as MultiAssayExperiment objects with integrated clinical and genomic data, along with an R package of utilities, and considerably improved access to these data by providing user-friendly analysis-ready objects.Built a collection of more than 20,000 uniformly processed shotgun metagenomic sequencing samples with harmonized clinical metadata across diverse chronic and infectious diseases for mega/meta-analysis that became the most downloaded microbiome data package on Bioconductor.