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.
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Rheinbay E, Nielsen MM, Abascal F, Wala JA, Shapira O, [...], Weischenfeldt J, Martincorena I, Pedersen JS, Getz G
Nature
578
(7793)
102-111
(2020).
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Hess JM, [...], Lawrence MS, Getz G
Cancer Cell
36
(3)
288-301
(2019).
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Yizhak K, [...], Getz G
Science
364
(6444)
(2019).
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Gopal RK, Kübler K, [...], Getz G, McFadden DG
Cancer Cell
34
(2)
242-255.e5
(2018).
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Haradhvala NJ, Kim J, Maruvka YE, [...], Mouw KW, Lawrence MS, Getz G
Nature Communications
9
(1)
1746
(2018).
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Chapuy B, Stewart C, Dunford AJ, [...], Getz G, Shipp MA
Nature Medicine
24
(5)
679-690
(2018).
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Maruvka YE, [...], Getz G
Nature Biotechnology
35
(10)
951-959
(2017).
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Dunford A, Weinstock D, [...], Lawrence MS, Getz G, Lane AA
Nature Genetics
49
(1)
10-16
(2016).
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Haradhvala NJ, Polak P, [...], Lawrence MS, Getz G
Cell
164
(3)
538-549
(2016).
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Landau DA, Tausch E, Taylor-Weiner AN, [...], Hallek M, Neuberg D, Getz G, Stilgenbauer S, Wu CJ
Nature
526
(7574)
525-530
(2015).