Julian Hess

Computational Biologist II
Google Scholar

Julian earned a B.A. from Williams College in Physics and Mathematics, where he studied algorithms for RNA secondary structure prediction. In the Getz Lab, his primary focus is on developing novel statistical methods for driver gene discovery (specifically, Bayesian hierarchical models and Monte Carlo techniques for sampling them). He also leads development of the lab’s computational infrastructure, consisting of (i) systems to deploy cloud-based high performance computing clusters comprising thousands of CPU cores, and (ii) APIs for interactively developing and efficiently dispatching large workflows comprising hundreds of thousands of jobs to run on these clusters. Secondary research interests include developing novel copy number calling, sequence alignment, and somatic variant calling methods.

Selected Lab Papers