Scientific data systems and AI engineering for biology.

Turns inconsistent scientific metadata into validated, provenance-aware structured data.

I designed an agentic workflow in which a supervisor coordinates schema inference, parsing, refinement, and validation. Typed contracts, retries, run manifests, and column-level provenance keep LLM-assisted transformations reviewable and suitable for downstream scientific systems.

Scientific AI systemScientific AI system · v1.1 · tested

Makes bulk RNA-seq quality control consistent, reviewable, and accessible before downstream analysis.

I built a modular R Shiny data product that validates count matrices, aligns sample metadata, calculates library and detection metrics, applies variance-aware PCA, and exports review-ready results. Golem structure, CI, and testthat coverage move QC beyond an ad hoc notebook.

RNA-seq quality controlResearch software · CI + tests

Encodes repeatable omics processing so cohort analyses can move from raw reads to inspectable outputs.

I developed modular Nextflow workflow prototypes for RNA-seq processing from read trimming and FastQC/MultiQC through STAR alignment and featureCounts, alongside genome-assembly and single-cell experiments. The emphasis is portable execution, explicit stages, and reproducible handoffs.

Scientific workflowsPortable, staged workflow prototypes

Tests whether experimental context improves bioactivity prediction beyond chemical structure alone.

In a collaborative Broad hackathon prototype, we integrated cell type, assay format, and readout metadata with molecular descriptors, then compared biologically informed QSAR strategies for IC50 prediction.

Context-aware QSARCollaborative research prototype · Broad hackathon

Explores target-specific QSAR modeling and compound prioritization from public bioactivity data.

Applies ChEMBL data, RDKit descriptors, and machine learning to model compound bioactivity against targets such as SARS-CoV 3C-like proteinase.

Cheminformatics workspaceIndependent research workspace · cheminformatics

Combines tissue spatial structure and gene expression to evaluate metastatic-potential prediction.

In a six-person University of Michigan project, I co-implemented, optimized, and trained a three-layer GCN built from spatial coordinates and a six-gene ECM signature. It reached AUC 0.91 on an imbalanced dataset; the report explicitly identifies external validation as the next requirement.

Spatial graph MLCollaborative course project · contribution documented

Tests whether a time-aware latent model preserves biological progression in single-cell expression data.

Uses a custom KL objective centered on capture time to shape a 10-dimensional latent space across 14,717 cells and 3,815 expression features, with paired UMAPs comparing true and posterior time structure.

Single-cell representation learningExploratory course project · model + visual evidence