I’m a PhD-trained scientist with a background in genome biology and computational analysis, driven by a curiosity to understand complex biological systems and a commitment to building tools and insights that support research and health innovation.

• Analyze genome structure, repetitive DNA, and chromatin states
• Build scalable pipelines for NGS data (ChIP-seq, RNA-seq, MNase-seq, TrAEL-seq)
• Develop tools in R, Python, and Bash, run on HPC clusters
• Extract biological insights from complex genomic regions
• Collaborate across wet and dry labs to drive discovery

What I’ve done:
I study the mechanisms of genome instability and chromatin remodeling using integrative analysis of next-generation sequencing data — including ChIP-seq, RNA-seq, MNase-seq, and TrAEL-seq. My research has centered on repetitive and telomeric regions, where I’ve built reproducible pipelines and combined experimental data with computational tools to uncover how genome architecture influences function and repair.

What I’m looking for:
I’m excited to bring this experience into translational genomics, multi-omics integration, and computational discovery in health and disease. I’m especially drawn to roles where I can contribute to biomedical insight, diagnostic innovation, or therapeutic development by working at the intersection of data science and biology.

• Scientist and bioinformatician roles in genomics, biotech, or diagnostics
• Computational biology and data science positions involving multi-omics or NGS
• Opportunities to build, scale, and apply bioinformatics pipelines in real-world contexts

R Python HPC Bioconductor Bash NGS ChIP-seq RNA-seq MNase-seq TrAEL-seq
Linux Nextflow Multi-omics Git/GitHub Workflow Automation Data Visualization

• ChromosomeEnds:
  Signal analysis scripts for ChIP-seq, RNA-seq, MNase-seq, TrAEL-seq used in the preparation of the manuscript

• python-bio-utils:
  Lightweight utility functions for analyzing genome-wide NGS data

• exploratory-analysis:
  R notebooks showcasing exploratory data analysis and visualization of genomic signals

• hwglabr2:
  R package for yeast genomics developed in Hochwagen lab (contributor)