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 Neuroscience

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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, 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.

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  

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: arXiv. You can see a demo here.


Our Variational HMC paper has been accepted by Bayesian Analysis.


Andrew Holbrook and Xu Gao were awarded the Graduate Dean's Dissertation Fellowship for 2017-2018.




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(949) 824-0623

2224 DBH, UC Irvine, CA 92697

babaks at uci dot edu

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