DIRECTOR, THE CENTER FOR STATISTICAL CONSULTING
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.
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).
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
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.
2224 DBH, UC Irvine, CA 92697
babaks at uci dot edu