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Introduction 👋

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. 🏥

💡 Some projects

Other tools 🔨

  • 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)
  • mdav: Data anonymization tool implementing maximum distance to the average vector (MDAV) anonymization (code)

Michael Zietz's Projects

broom icon broom

Convert statistical analysis objects from R into tidy format

gwas_replication icon gwas_replication

Short investigation of GWAS replicability in T2D for EUR/JPN/AFR populations

igwas icon igwas

Fast GWAS on linear combinations of traits using only summary statistics - Rust re-implementation

ldsc icon ldsc

LD Score Regression (LDSC)

map-modifiers icon map-modifiers

Severity-focused biomedical concept normalization in Python

mdav icon mdav

Data anonymization using maximum distance to the average vector (MDAV)

mtag icon mtag

Python command line tool for Multi-Trait Analysis of GWAS (MTAG)

prs-portability icon prs-portability

Portability of polygenic risk scores between and within populations

pymbend icon pymbend

Bending non-positive-definite matrices to positive-definite

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