Dillon Gavlock is a Medical Data Scientist III with nine years of interdisciplinary experience applying machine learning and image analysis to drug discovery and biomedical research from a lab-bench to computational pipelines. At the University of Pittsburgh he has built automated imaging and NGS pipelines, developed novel quantification algorithms across diverse disease models (NAFLD, COVID-19, metastatic cancers), and translated experimental workflows into publishable results. He blends hands-on wet-lab expertise with advanced computational methods—developing ligand-free pharmacophore generation and fragment-docking workflows during graduate work—to speed virtual screening for drug targets. Creator of the Open-Source-Comp-Bio-Masters initiative, he contributes to community knowledge in computational biology while actively bridging collaboration between academia and software engineering. Based in Pittsburgh, he is as comfortable iterating on experimental protocols as he is deploying reproducible analysis pipelines that scale to large immunohistochemical and sequencing datasets.
9 years of coding experience
3 years of employment as a software developer
Bachelor’s Degree, Biomedical Sciences, Bachelor’s Degree, Biomedical Sciences at Lock Haven University of Pennsylvania
Master's degree, Computational Biotechnology and Biomedicine, Master's degree, Computational Biotechnology and Biomedicine at University of Pittsburgh School of Medicine
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