Babak Shahbaba

Babak Shahbaba

Associate Professor

Departments of Statistics and Computer Science

University of California, Irvine

Scalable Bayesian Inferences

Nonparametric Bayesian Methods

Statistical Methods in Biological Sciences




Zhou, B., Moorman, D. E., Behseta, S., Ombao, H., and Shahbaba, B. (2016), A Dynamic Bayesian Model for Characterizing Cross-Neuronal Interactions During Decision Making, Journal of the American Statistical Association, 111 (514), 459-471.

Holbrook, A., Vandenberg-Rodes, A., Fortin, N., Shahbaba, B. (2017), A Bayesian supervised dual-dimensionality reduction model for simultaneous decoding of LFP and spike train signals, Stat, 6 (1), 53-67.

Zhang, C., Shahbaba, B., Zhao, H. (2017), Hamiltonian Monte Carlo Acceleration Using Surrogate Functions with Random Bases, Statistics and Computing, 27, 1473-1490.

Zhang, C., Shahbaba, B., Zhao, H. (2017), Precomputing strategy for Hamiltonian Monte Carlo Method based on regularity in parameter space, Computational Statistics, 32(1), 253-279.


STATS 230, Statistical Computing Methods (Winter 2017): Numerical linear algebra; Optimization methods; Sampling algorithms; EM; Bootstrap

STATS 200B, Intermediate Probability and Statistical Theory (Winter 2017): Random samples; Limit laws; Stochastic processes; Estimation

Workshop on Introduction to Biostatistics (Spring 2016): Design and analysis of scientific studies; Data exploration; Probability; Estimation; Hypothesis testing

STATS 235, Modern Data Analysis (Statistical Machine Learning, Spring 2016): Overview of some general concepts in statistics and machine learning; Overview of optimization and sampling algorithms; Supervised and unsupervised learning; Statistical learning; Regularization; Splines; Gaussian process regression models; Neural  networks; SVM; Tree-based methods


We received an NIH grant (R01MH115697) to develop novel methods for neural data analysis. This is a collaboration with Dr. Hernando Ombao and Dr. Norbert Fortin.

Andrew Holbrook received the UCI MIND award for his work in theoretical mathematics, probability, statistics, and neuroscience.

I will be presenting at the 9th International Purdue Symposium on statistics

I will give a talk at the Kyoto Deep Learning workshop on March 19.

Our new paper on dynamic modeling of covariance matrices is available on arXiv. You can also see a demo here.

Our Variational HMC paper has been published in Bayesian Analysis.

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(949) 824-0623

2224 DBH, UC Irvine, CA 92697

babaks at uci dot edu