Hey, I'm Daniel

Technical Leader · ML Engineer · Bioinformatics Scientist

I'm a machine learning engineer and technical leader with a BS in Computer Science from DePaul University and an MS in Data Science from the University of Chicago. I specialize in designing and scaling deep learning systems for large-scale bioinformatics problems. I'm focused on building high-impact AI teams and translating cutting-edge research into real-world solutions.

Research

Publications

Peer-reviewed research I've contributed to.

Co-Author Microorganisms 2025
teamNGS Balances Sensitivity for Viruses with Comprehensive Microbial Detection in Clinical Specimens
Yamaguchi J, Orf GS, Malinauskas J, Mata M, Weiss SL, Forberg K, Meyer TV, Wiebe PO, Mowerman I, Piotrowski SJ, Glownia D, Rodgers MA, Hackett J Jr, Suputtamongkol Y, Phoompoung P, Gomathi S, Pradeep A, Solomon SS, Bbosa N, Kaleebu P, Ahouidi AD, Mboup S, Sequeira AF, Tojo A, Cloherty GA, Berg MG.

Probe-based capture represents a highly sensitive and cost-effective approach for overcoming host background and enriching viruses in metagenomic NGS (mNGS) libraries. Using clinical specimens collected globally from patients with fever or respiratory illness, teNGS achieved increased sensitivity with 100–10,000× increases in depth and >50% genome coverage for pathogens with titers ≥ 1000 cp/mL. teamNGS couples target enrichment with standard mNGS into a single sequencing run, improving genome recovery while maintaining comprehensive non-viral microbial detection.

PubMed PMC Free Article DOI: 10.3390/microorganisms13122854