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Zachary McCaw

Staff Machine Learning Scientist

Hometown:

Chapel Hill, NC

Education:

Ph.D., A.M. in biostatistics, Harvard University; Graduate certificate in artificial intelligence, Stanford University; B.S./B.S.P.H. in quantitative biology and biostatistics, University of North Carolina at Chapel Hill

Previous Experience:

I was previously a data scientist at Google.

Responsibilities at insitro:

I am the oncology genetics technical lead and focus on statistical methods development.

Key Papers:

An allelic-series rare-variant association test for candidate-gene discovery

Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk models

Leveraging a machine learning derived surrogate phenotype to improve power for genome-wide association studies of partially missing phenotypes in population biobanks

Practical Recommendations on Quantifying and Interpreting Treatment Effects in the Presence of Terminal Competing Risks: A Review

DeepNull models non-linear covariate effects to improve phenotypic prediction and association power

Large-scale machine-learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology

Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies