Nathan Lapierre
Research Fellow at Dana-Farber Cancer Institute
United States
Summary
👤
Senior🎓
Top SchoolNathan LaPierre is a computational biologist and postdoctoral research fellow at Harvard Medical School and Dana-Farber Cancer Institute with 11 years of experience bridging machine learning, statistical genetics, and genomics. He develops deep generative models applied to single-cell data to make causal predictions of perturbation effects, building on prior work in Mendelian randomization, fine-mapping, disease prediction with deep learning, and metagenomics. His trajectory spans impactful academic research from a PhD at UCLA through postdoctoral roles at the University of Chicago, and includes early bioinformatics contributions such as the CAMIL multiple-instance learning pipeline for microbiome-based disease prediction. Comfortable moving between software engineering and statistical methodology, he brings practical systems experience from industry internships and a history of publishing in competitive bioinformatics venues. Colleagues describe him as someone who combines theoretical rigor with hands-on model engineering to translate high-dimensional biology into testable causal hypotheses.
11 years of coding experience
8 years of employment as a software developer
Advanced Studies Diploma, Advanced Studies Diploma at Thomas Jefferson High School for Science and TechnologyUniversity of California, Los AngelesBachelor of Science (B.S.) Computer Software Engineering, Bachelor of Science (B.S.) Computer Software Engineering at George Mason University
English
Github Skills (28)
minhash10
fine-mapping10
profiling9
bioinformatics9
gwas8
conda8
package-management8
bioconda7
statistics6
python5
causal5
medicine4
react3
r-package3
matlab2
Programming languages (10)
Github contributions (5)
nlapier2/MiniScrub
Jul 2017 - Dec 2017
Contributions:46 commits in 4 months
nlapier2/multiInstanceLearning
Jul 2016 - Feb 2017
Contributions:32 commits, 13 pushes in 7 months
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