Hi, I’m Edwin — an AI / Machine Learning Engineer with 3+ years of experience building and deploying production ML systems. I specialize in applied machine learning, ML pipelines, and LLM-powered tools data-intensive applications, with a focus on reliability, scalability, and real-world impact.

• Production ML pipelines and reproducible workflows
• Applied ML for biomedical and health data
• GPU acceleration and performance optimization
• LLM integration into real-world systems

Luebeck J, Huang E, et al.
AmpliconSuite: Analyzing Focal Amplifications in Cancer Genomes.
Genomics and Informatics, 2024.
🔗 ScienceDirect
→ Deployed ML pipelines and a cloud-hosted repository for large-scale ecDNA analysis, enabling reproducible research across 20 + projects (funded >$25 M).

Liefeld T, Huang E, Wenzel A, Yoshimoto K, Sharma A, Sicklick J, Mesirov J, Reich M.
NMF Clustering: Accessible NMF-based Clustering Utilizing GPU Acceleration.
Genomics and Informatics, 2024.
🔗 Fortune Journals
→ Implemented RAPIDS AI, CuPy, and custom CUDA kernels to achieve 27× runtime speedup on HPC clusters.

Reich M, Tabor T, Liefeld J, Joshi J, Kim F, Huang E, Thorvaldsdottir H, Blankenberg D, Mesirov J.
Genomics to Notebook (g2nb): Extending the Electronic Notebook to Address the Challenges of Bioinformatics Analysis.
Genomics and Informatics, 2024.
🔗 Fortune Journals
→ Contributed to extending Jupyter-based infrastructure (g2nb) for scalable bioinformatics workflows.

Reich M, Tabor T, Liefeld J, Huang E, Kim F, Mesirov J.
The GenePattern Ecosystem for Cancer Bioinformatics.
AACR Cancer Research (Abstract 7426), 2024.
🔗 AACR Abstract
→ Presented cloud-based GenePattern workflows supporting cancer informatics and LLM integration.