Computational Biology · Statistical Genetics · Machine Learning
Computational Biologist & Data Scientist
I build statistical and machine-learning models that turn multi-omic, single-cell, and clinical data into decisions — from biomarker readouts in the clinic to risk prediction in the NICU. Ten-plus years across genomics, drug discovery, and applied ML, with a patent and peer-reviewed work to show for it.
About
My PhD at Ohio State was in computational biology with a statistical genetics focus — small-sample and rare-event inference, work that ended up peer-reviewed in the American Journal of Epidemiology. That grounding shows up in everything since: I build models that are accurate, but also honest about their own uncertainty.
Since then I've worked across biomarker analysis, single-cell and multi-omics, and ML pipeline development — a Senior Scientist role at Amgen, computational biology and data science at Sensorium Therapeutics, and postdoctoral research in HIV genomics. I hold a patent in neonatal complication prediction, filed through Nationwide Children's Hospital.
I care about the unglamorous parts: reproducible Nextflow pipelines, honest evaluation on imbalanced data, and code a colleague can read six months later. I work best sitting between the wet lab and the cluster, translating what came off the instrument into something a team can act on.
Experience
Apr 2025 — May 2026
Amgen
Sep 2024 — Apr 2025
Sensorium Therapeutics
Sep 2022 — Sep 2024
Sensorium Therapeutics
Mar 2022 — Aug 2022
Henry M. Jackson Foundation
Aug 2016 — Mar 2022
The Ohio State University
Selected projects
Each of these started as a modeling question and ended as a number I'm willing to defend — including the corrections that made the number smaller.
87%
AUC, corrected
Polygenic risk score from 182 sequenced exomes in preterm infants (26–31 weeks), combined with gestational age. Raw AUC of 96% dropped to 87% once I corrected for over-fitting — the honest number, and the one that matters. Patented (WO2021061697A1) and published in Pediatric Research.
760
MoA classes
Random-forest model classifying compounds into 760 distinct mechanism-of-action classes from L1000 gene-expression signatures, at over 80% accuracy. Built to give chemists a first read on what a new compound is probably doing.
0.96
AUC
Random-forest classifier predicting BBB permeability from compound features, built to prioritize candidates earlier in the discovery pipeline. The features that carry the model — logP, molecular weight, TPSA — are exactly the ones medicinal chemists already reason about, which is what made it trustworthy to them.
~10%
true KO rate
CRISPR screen analysis with pertpy: Mixscape to separate true knockouts
from escapers — only about one cell in ten was really perturbed — then E-distance to
rank effect size and an E-test for significance. Top hits recover the IFN-γ/JAK-STAT
axis with correct STAT1 specificity, plus a genetic-interaction table calling synergy
vs. suppression in double perturbations.
98%
cell-type accuracy
Benchmarked PCA vs. Harmony vs. scVI on the heart cell atlas using batch-mixing entropy and cell-type silhouette. Harmony wins on mixing, scVI preserves more biology, and both beat raw PCA — a genuine trade-off rather than a winner. Then scANVI for label transfer, reaching 98% on held-out cells, with errors concentrated in rare and similar types.
0.944
ROC-AUC
Scored AlphaMissense pathogenicity predictions against ClinVar labels across ten disease genes. The headline AUC is strong — but the interesting result is the failure mode: NPC1 I1061T, a common and firmly pathogenic Niemann-Pick C1 mutation, scores 0.303 and is called benign. Aggregate metrics hide the misses that matter clinically.
~90%
accuracy
ResNet transfer-learning model for binary gate classification, fine-tuned with early layers frozen to adapt a general-purpose image model to flow-cytometry data — a practical way to get a useful classifier out of a small labelled set.
2×
eval tiers
Retrieval-augmented QA over real PubMed abstracts, with cited answers and a hallucination guard that refuses when the retrieved context doesn't support a claim. Evaluated in two tiers — Recall@k and MRR for the retriever, then LLM-as-judge faithfulness and answer relevancy for the generator, because a good retriever and a good answer are different failures.
Skills