Jeff Wintersinger
Principal Scientist at Deep Genomics
Old Toronto, Ontario, Canada
Summary
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Senior🎓
Top SchoolJeff Wintersinger is a Principal Scientist and computational biologist with 17 years of experience applying scalable machine learning and software engineering to cancer genomics and biomedical problems. Currently leading research at Deep Genomics after a PhD in Computer Science at the University of Toronto, he bridges rigorous algorithm development with practical pipelines for high-performance computing and interactive visualizations that make results accessible to biologists and clinicians. He has a track record of rapidly iterating analyses alongside non-computational collaborators, building robust ML methods that survive noisy biological data and production constraints. Trained in bioinformatics and health sciences, he combines domain insight with systems thinking to move models from prototype to reproducible, high-throughput workflows. An often-overlooked strength is his emphasis on intuitive visualization design as a scientific tool, not just a presentation layer.
17 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, 4.00, Doctor of Philosophy - PhD, Computer Science, 4.00 at University of TorontoBachelor of Health Sciences, Bioinformatics, 3.84, Bachelor of Health Sciences, Bioinformatics, 3.84 at University of Calgary
Github Skills (48)
genome10
composition10
evolutionary10
cancer-genomics10
genetic10
infectious-diseases10
evolution10
sequencing10
bioinformatics10
genomics10
uncertainty10
org-mode9
visualization9
scaling9
graph9
Programming languages (5)
Github contributions (5)
jwintersinger/kablammo
Jun 2013 - Sep 2022
BLAST results visualization tool
Contributions:229 commits, 4 pushes in 9 years 4 months
cancer-genomicsvisualization
morrislab/pairtree
May 2017 - Nov 2021
Pairtree is a method for reconstructing cancer evolutionary history in individual patients, and analyzing intratumor genetic heterogeneity. Pairtree focuses on scaling to many more cancer samples and cancer cell subpopulations than other algorithms, and on producing concise and informative interactive characterizations of posterior uncertainty.
Contributions:1 review, 873 commits, 2 PRs in 4 years 6 months
analyzingindividualposteriorgeneticinformative
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