Biochemist working at the intersection of protein science and data analysis. I design assays, optimize workflows, and use computational tools to make sense of the results.
Good experiments are designed for analysis from the start. Clean plate maps, systematic controls, and reproducible workflows turn noise into signal.
Projects combining protein biochemistry, assay development, and computational analysis.
Led computational-experimental optimization of protein-binding peptides, achieving 6-fold affinity improvement. Used structure predictions to guide design and fluorescence-based assays for validation.
Developed 384-well fluorescence assay for bacterial chaperone complex. Executed pilot screen of 1,857 compounds using automated liquid handling, delivering 253 validated hits.
Characterized binding and stability of de novo designed protein mimics in collaboration with computational design groups. Bridged structural predictions with experimental validation.
Analysis workflows for exploring data, fitting models, and quality control.
Approach: Code should make experimental logic visible and workflows reproducible. Each notebook tells a story: what was measured, how it was analyzed, and whether results make sense.
Import tabular data, clean outliers, fit dose-response curves, validate assumptions. Includes diagnostic plots and goodness-of-fit checks.
Automated quality control for 384-well screens. Computes plate statistics, flags suspect wells, generates heatmaps for batch processing.
Generate publication-quality SAR figures. Integrates chemical structure rendering with activity data plotting.
Visual documentation from the lab, travel, and everyday moments.
More photos on Instagram (@hsphoto70)
Writing about science, career transitions, and computational approaches to biology.
I write on Substack about the intersection of experimental science and data analysis, navigating career transitions in biotech, and lessons learned from building reproducible workflows. Topics range from assay troubleshooting to job market realities.
Read on Substack →Background, approach, and what I'm looking for.
Biochemist with experience in protein assay development and computational analysis. I work at the intersection of wet lab and data science—designing experiments that produce clean, analyzable datasets.
My approach: build workflows that produce model-ready data from the start. Think about analysis during experimental design. Choose controls that test assumptions. Document like someone else will need to reproduce it.
Looking for roles combining protein biochemistry, data analysis, and scientific communication. Geographically focused on Southern California (LA/OC, San Diego), with secondary interest in Seattle/Portland.
I also write about science and career development on my Substack.