BABAK SHAHBABA

ASSOCIATE PROFESSOR

DIRECTOR, THE CENTER FOR STATISTICAL CONSULTING

UCI

Scalable Bayesian Inferences

Nonparametric Bayesian Methods

Statistical Methods in Neuroscience

HIGHLIGHTS

Research

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 (to appear).

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

Zhang, C., Shahbaba, B., Zhao, H. (2016), Precomputing strategy for Hamiltonian Monte Carlo Method based on regularity in parameter space, Computational Statistics (to appear).

Teaching

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

News

Xu Gao's work (with Hernando Ombao and Norbert Fortin) on evolutionary state space models for LFP won the student paper awards at JSM and ENAR.

New workshop on Big Data in Data Science sponsored by the Data Science Initiatives

Talk at MCQMC (2016, Stanford): Wormhole Hamiltonian Monte Carlo

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

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.

(949) 824-0623

2224 DBH, UC Irvine, CA 92697

babaks at uci dot edu

Contact