James Johndrow
James Johndrow
Assistant Professor, Department of Statistics, University of Pennsylvania
Verified email at
Cited by
Cited by
The Type I IFN response to infection with Mycobacterium tuberculosis requires ESX-1-mediated secretion and contributes to pathogenesis
SA Stanley, JE Johndrow, P Manzanillo, JS Cox
The Journal of Immunology 178 (5), 3143-3152, 2007
Anti-inflammatory actions of lipoxin A4 and aspirin-triggered lipoxin are SOCS-2 dependent
FS Machado, JE Johndrow, L Esper, A Dias, A Bafica, CN Serhan, ...
Nature medicine 12 (3), 330-334, 2006
An algorithm for removing sensitive information
JE Johndrow, K Lum
The Annals of Applied Statistics 13 (1), 189-220, 2019
Coordination of microtubule and microfilament dynamics by Drosophila Rho1, Spire and Cappuccino
AE Rosales-Nieves, JE Johndrow, LC Keller, CR Magie, DM Pinto-Santini, ...
Nature cell biology 8 (4), 367-376, 2006
Scalable approximate MCMC algorithms for the horseshoe prior
J Johndrow, P Orenstein, A Bhattacharya
Journal of Machine Learning Research 21 (73), 2020
Sisyphus, the Drosophila myosin XV homolog, traffics within filopodia transporting key sensory and adhesion cargos
R Liu, S Woolner, JE Johndrow, D Metzger, A Flores, SM Parkhurst
Oxford University Press for The Company of Biologists Limited 135 (1), 53-63, 2008
MCMC for imbalanced categorical data
JE Johndrow, A Smith, N Pillai, DB Dunson
Journal of the American Statistical Association 114 (527), 1394-1403, 2019
The Hastings algorithm at fifty
DB Dunson, JE Johndrow
Biometrika 107 (1), 1-23, 2020
Rho GTPase function in flies: insights from a developmental and organismal perspective
JE Johndrow, CR Magie, SM Parkhurst
Biochemistry and cell biology 82 (6), 643-657, 2004
Tensor decompositions and sparse log-linear models
JE Johndrow, A Bhattacharya, DB Dunson
Annals of statistics 45 (1), 1, 2017
Approximations of Markov chains and high-dimensional Bayesian inference
JE Johndrow, JC Mattingly, S Mukherjee, D Dunson
arXiv preprint arXiv:1508.03387, 2015
Error bounds for approximations of Markov chains used in Bayesian sampling
JE Johndrow, JC Mattingly
arXiv preprint arXiv:1711.05382, 2017
Scaling up data augmentation MCMC via calibration
LL Duan, JE Johndrow, DB Dunson
The Journal of Machine Learning Research 19 (1), 2575-2608, 2018
Diagonal orthant multinomial probit models
J Johndrow, D Dunson, K Lum
Artificial Intelligence and Statistics, 29-38, 2013
Genetic diversity does not explain variation in extra‐pair paternity in multiple populations of a songbird
IA Liu, JE Johndrow, J Abe, S Lüpold, K Yasukawa, DF Westneat, ...
Journal of evolutionary biology 28 (5), 1156-1169, 2015
Estimating the number of SARS-CoV-2 infections and the impact of mitigation policies in the United States
J Johndrow, P Ball, M Gargiulo, K Lum
Harvard Data Sci. Rev 10, 2020
Drosophila Rho-kinase (DRok) is required for tissue morphogenesis in diverse compartments of the egg chamber during oogenesis
V Verdier, JE Johndrow, M Betson, GC Chen, DA Hughes, SM Parkhurst, ...
Developmental biology 297 (2), 417-432, 2006
Optimal approximating Markov chains for Bayesian inference
JE Johndrow, JC Mattingly, S Mukherjee, D Dunson
arXiv preprint arXiv:1508.03387, 2015
Theoretical limits of microclustering for record linkage
JE Johndrow, K Lum, DB Dunson
Biometrika 105 (2), 431-446, 2018
No free lunch for approximate MCMC
JE Johndrow, NS Pillai, A Smith
arXiv preprint arXiv:2010.12514, 2020
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