Babak Shahbaba






Scalable Bayesian Inferences

Nonparametric Bayesian Methods

Statistical Methods in Neuroscience



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

Zhang, C., Shahbaba, B., Zhao, H. (2016), Hamiltonian Monte Carlo Acceleration Using Surrogate Functions with Random Bases, Statistics and Computing (to appear)

Agostinelli, F., Ceglia, N., Shahbaba, B., Sassone-Corsi, P., Baldi, P., What Time is it? Deep Learning Approaches for Circadian Rhythms (2016), Bioinformatics, 32(12), i8-i17.

Lan, S., Palacios, J., Karcher, M., Minin, V., Shahbaba, B. (2015) An Efficient Bayesian Inference Framework for Coalescent-Based Nonparametric Phylodynamics, Bioinformatics, 31(20), 3282-3289.

Vandenberg-Rodes, A. and Shahbaba, B. (2015), Dependent Matérn Processes for Multivariate Time Series, arXiv:1502.03466.


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


MCQMC (August 2016, Stanford): Wormhole Hamiltonian Monte Carlo

ICERM, Stochastic numerical algorithms, multiscale modeling and high-dimensional data analytics (July 2016, Brown University): Variational Hamiltonian Monte Carlo

UCLA, Biostatistics (October, 2015): Scalable Bayesian Inference

University of Texas at Austin, (October, 2015): Scalable Bayesian Inference

New NSF grant (DMS 1622490):

Theory and practice for exploiting the underlying structure of probability models in big data analysis. The objective of this project is to combine geometric methods with computational algorithms in order to scale up statistical methods used for big data analysis.

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(949) 824-0623

2224 DBH, UC Irvine, CA 92697

babaks at uci dot edu