Hi there! I'm Michael Zietz. I'm a data scientist and genetics researcher, focused on developing new statistical and machine learnings methods with applications in healthcare and biomedicine. 🧬 I'm currently a Research Data Scientist at Cedars-Sinai Computational Biomedicine. Previously, I did my PhD at Columbia University DBMI and studied Physics at Penn, where I did research on heterogeneous networks in the Greene Lab. 🕸️ I'm interested in methods development, reproducible research, and accelerating the pace of scientific progress on complex diseases. 🏥
- Indirect GWAS: Fast GWAS on linear combinations of traits (code, analysis, preprint)
- MaxGCP: Optimal phenotyping for research in complex disease genetics (code, analysis)
- COVID Blood type: Study of the relationship between ABO type and COVID-19 (analysis, paper, New York Times)
- XSwap: A fast implementation of degree-preserving network randomization (code, analysis, paper)
- sumher_rs: Efficiently estimating a genetic covariance matrix using SumHer (code)
- pymbend: Bending matrices to be positive semi-definite (code)
- Micromanubot: A user-friendly build tool for academic preprints in LaTeX (code)
- clock: Dead-simple time tracking tool (
clock in work,clock out) (code) - annotate: Terminal user interface for text annotation (code)