Software Engineer and hobbyist Computational Biologist building at the intersection of genomics, bioinformatics, and health AI.
- 🧬 Author of OSGenome and OSGenome v2 : a genomic analysis framework for 23andMe SNP data, featured in Harvard Medical School's BioGrids registry (v1)
- 📄 Co-author of two peer-reviewed Application Notes in Oxford Bioinformatics
- 🎓 BS Computer Science (Minor: Psychology) · MS Software Development
- 📚 Currently completing Stanford School of Medicine's AI in Healthcare specialization on Coursera
- 🏥 Focused on applying AI and machine learning to clinical and genomic data
- Extending OSGenome with local AI integration for privacy-first genomic interpretation
- Exploring ML applications in computational biology and clinical genomics
- Building at the intersection of bioinformatics and modern AI tooling
Languages: Python, C#, Java
Bioinformatics: SNP analysis, genomic data pipelines
ML/AI: scikit-learn, numpy, llama-cpp-python, HuggingFace
Tools: Git, Visual Studio, Visual Studio Code
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MOST – Visualization: Software for producing automated textbook-style maps of genome-scale metabolic networks Co-Author April 2017 Bioinformatics, Oxford Journals Visualization of metabolites, reactions and pathways in genome-scale metabolic networks (GEMs) can assist in understanding cellular metabolism. Three attributes are desirable in software used for visualizing GEMs: 1. automation, since GEMs can be quite large; 2. production of understandable maps that provide ease in identification of pathways, reactions, and metabolites; and 3. visualization of the entire network to show how pathways are interconnected. No software currently exists for visualizing GEMs that satisfies all three characteristics, but MOST-Visualization, an extension of the software package MOST (Metabolic Optimization and Simulation Tool), satisfies (1), and by using a pre-drawn overview map of metabolism based on the Roche map satisfies (2) and comes close to satisfying (3).
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MOST: A software environment for constraint-based metabolic modeling and strain design Co-Author October 14, 2014 Bioinformatics, Oxford Journals MOST (Metabolic Optimization and Simulation Tool) is a software package that implements GDBB (Genetic Design through Branch and Bound) in an intuitive user-friendly interface with Excel-like editing functionality, as well as implementing FBA (Flux Balance Analysis), and supporting SBML (Systems Biology Markup Language) and CSV (Comma-Separated Values) files. GDBB is currently the fastest algorithm for finding gene knockouts predicted by FBA to increase production of desired products, but GDBB has only been available on a command line interface, which is difficult to use for those without programming knowledge, until the release of MOST.