West Lafayette, Indiana, United States
Computational biologist with 15 years of experience analyzing and interpreting genomics and epigenomics datasets, with emphasis on generating publication-quality figures that support defensible biological conclusions using reproducible workflows. Deep practical experience across ChIP-seq, ATAC-seq, CUT&RUN, CUT&Tag, BS-seq, MNase-seq, NOMe-seq, RNA-seq, and related high-throughput sequencing assays, including work in the Anderson, Jones, Shilatifard, and Kadoch laboratories. Particularly well aligned with biological AI benchmark development: experienced in making practical epigenomics analysis decisions around dataset quality, differential accessibility and occupancy, transcription factor motif enrichment, analysis of motif-count / motif-density and chromatin structure, chromatin-state interpretation, cis-regulatory analysis and whether computational outputs support defensible regulatory conclusions. Current professional development focuses on Python/Jupyter, data science fundamentals, generative AI-assisted analysis, and portfolio-ready reproducible notebooks. SKILLS AND INTERESTS · Epigenomics and chromatin assays: ChIP-seq, ATAC-seq, CUT&RUN, CUT&Tag, MNase-seq, NOMe-seq, BS-seq, DNase-seq, RNA-seq, and 16S rRNA-seq. · Regulatory genomics: peak calling and QC interpretation, differential accessibility/occupancy, transcription factor motif enrichment, customized motif-count/motif-density method, partitioning of data aligned to peaks into groups, and integrating ChIP-seq or CUT&RUN/Tag with RNA-seq. · Biological domains: histone modifications & variants, chromatin architecture, transcription factors, DNA methylation, nucleosomes, cancer, neurodevelopment, immunology, and virology. · Computational environments: Unix/Linux, high-performance computing clusters, large-scale sequencing data management, reproducible analysis workflows, custom scripting, and collaborator-facing reports. · Programming and analysis: R, Perl, Unix shell, custom statistical analysis, figure generation, motif enrichment tools, and current Python/Jupyter/genAI data science refresh. · Current analysis platform: Visual Studio Code with OpenAI Codex support, Jupyter notebooks, and Anaconda-managed Python kernels for reproducible, AI-assisted data analysis workflows. · Scientific communication: publication-oriented visualization, analytical storytelling, manuscript support, collaborator meetings in person and online, and figure refinement. SELECTED FIGURE PORTFOLIO drive.google.com/drive/folders/1PqoUHB28P7cgw9foCvQtrhQIqR7yq7IQ