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Babak Shahbaba
Researcher at University of California, Irvine
Publications - 104
Citations - 2696
Babak Shahbaba is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Hybrid Monte Carlo & Bayesian inference. The author has an hindex of 24, co-authored 98 publications receiving 2271 citations. Previous affiliations of Babak Shahbaba include University of Toronto & Stanford University.
Papers
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Journal ArticleDOI
Maternal cortisol over the course of pregnancy and subsequent child amygdala and hippocampus volumes and affective problems
TL;DR: Higher maternal cortisol levels in early gestation was associated with more affective problems in girls, and this association was mediated, in part, by amygdala volume, while no association between maternal cortisol in pregnancy and child hippocampus volume was observed in either sex.
Journal Article
Nonlinear Models Using Dirichlet Process Mixtures
Babak Shahbaba,Radford M. Neal +1 more
TL;DR: In this article, the Dirichlet process mixtures are used to model the joint distribution of response variable, y, and covariates, x, non-parametrically using Dirichlets.
Journal ArticleDOI
Maternal psychosocial stress during pregnancy is associated with newborn leukocyte telomere length
Sonja Entringer,Elissa S. Epel,Jue Lin,Claudia Buss,Babak Shahbaba,Elizabeth H. Blackburn,Hyagriv N. Simhan,Pathik D. Wadhwa +7 more
TL;DR: The finding provides the first preliminary evidence in human beings that maternal psychological stress during pregnancy may exert a "programming" effect on the developing telomere biology system that is already apparent at birth, as reflected by the setting of newborn LTL.
Journal ArticleDOI
Neural Function, Injury, and Stroke Subtype Predict Treatment Gains After Stroke
Erin Burke Quinlan,Lucy Dodakian,Jill See,Alison McKenzie,Vu Le,Michael Wojnowicz,Babak Shahbaba,Steven C. Cramer +7 more
TL;DR: It is hypothesized that a multivariate approach incorporating these 3 measures would have the greatest predictive value when treating human subjects with restorative therapies poststroke.
Proceedings Article
Distributed Stochastic Gradient MCMC
TL;DR: This work argues that stochastic gradient MCMC algorithms are particularly suited for distributed inference because individual chains can draw mini-batches from their local pool of data for a flexible amount of time before jumping to or syncing with other chains, which greatly reduces communication overhead and allows adaptive load balancing.