David Knowles
Associate Professor
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolDavid Knowles is an associate professor and computational genomics researcher with 12 years of experience applying Bayesian machine learning to functional genomics. Trained under Zoubin Ghahramani at Cambridge and with postdoctoral work at Stanford, he develops novel probabilistic and variational methods to dissect genetic and environmental influences on gene expression and RNA splicing. Based at Columbia and the New York Genome Center, he leads a lab translating statistical methodology into tools for large-scale genomic studies. His background spans academic rigor and industry-style engineering from Microsoft Research Cambridge, reflecting a blend of theory, software craftsmanship, and applied biology. Notably, his work bridges Bayesian nonparametrics with practical genomics problems, yielding methods tailored to complex, noisy biological data.
12 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy (PhD), Machine learning, Doctor of Philosophy (PhD), Machine learning at University of CambridgeMSc, Bioinformatics and Systems Biology, MSc, Bioinformatics and Systems Biology at Imperial College London
Github Skills (143)
python10
seq10
bedtools10
minhash10
rna-seq10
yang10
genome10
bayesian10
jack10
machine-learning10
bioinformatics10
bam9
neural-network9
probabilistic-programming9
gpu9
Programming languages (12)
Github contributions (5)
davidaknowles/tensor_gp
Mar 2016 - Feb 2017
Contributions:29 commits, 22 pushes, 1 branch in 11 months
davidaknowles/leafcutter
Nov 2015 - Feb 2021
Annotation-free quantification of RNA splicing. Yang I. Li, David A. Knowles, Jack Humphrey, Alvaro N. Barbeira, Scott P. Dickinson, Hae Kyung Im, Jonathan K. Pritchard
Contributions:7 releases, 1 review, 261 commits in 5 years 3 months
splicingyangbioinformaticsrna-seqquantification
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