Will Dampier
Associate Professor
Philadelphia, Pennsylvania, United States
Summary
🤩
Rockstar🎓
Top SchoolWill Dampier is an Associate Professor and PhD-trained biomedical engineer with 16 years of experience building scalable genomics, CRISPR gene-editing, and ML solutions that power translational HIV research. He has led NIH-funded projects exceeding $20M, managed a nanopore sequencing core and multiple HPC clusters, and authored production Snakemake toolkits used across labs. Will blends hands-on engineering—developing genome-scale Cas9 pipelines, transformer-based models like HIV-BERT, and back-end contributions to data-visualization tooling—with deep mentorship, training 50+ students and 20+ trainees in ‘omics and biostatistics. Based in Philadelphia, he’s focused on translating academic methods into deployable gene-editing infrastructure and AI-driven genomic prediction, with a practical knack for shipping reproducible, high-throughput pipelines. An often-overlooked strength is his track record in operationalizing core facilities and tooling so teams can scale science without reinventing the computational stack.
15 years of coding experience
18 years of employment as a software developer
PhD, Bioinformatics, PhD, Bioinformatics at Drexel University
python, matlab, c, ruby
Github Skills (11)
data-visualizations10
categorical-data10
data-visualization10
data-visualisation10
python10
data-science10
linked-data10
numpy9
pandas9
testing8
pytest8
Programming languages (6)
Github contributions (5)
glue-viz/glue
Jan 2014 - Jul 2015
Linked Data Visualizations Across Multiple Files
Role in this project:
Back-end Developer / Data Scientist
Back-end Developer / Data ScientistContributions:44 commits, 3 PRs, 20 comments in 1 year 5 months
Contributions summary:Will primarily contributed to the core functionalities of the 'glue' library, focusing on data manipulation and visualization. Their work involved fixing bugs related to component retrieval and handling of None values within the data framework. They also implemented categorical data handling, a critical feature for data analysis, and refactored axis tick formatting. The user demonstrated expertise in Python and data structures by modifying the core functionality of the linked data visualization project.
data-analysispythonlinked-datadata-sciencemultiple-files
JudoWill/PySeqUtils
Feb 2013 - Jan 2014
Contributions:170 commits in 10 months
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