HIGHLIGHTS

Research

Gao, X,, Shen, W., Shahbaba, B., Fortin, N.J., Ombao, H. (2018), Evolutionary State-Space Model and Its Application to Time-Frequency Analysis of Local Field Potentials, Statistica Sinica (to appear).

Li, L., Holbrook, A., Shahbaba, B, Baldi, P. (2019), Neural Network Gradient Hamiltonian Monte Carlo, Computational Statistics (to appear).

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. (2017), Hamiltonian Monte Carlo Acceleration Using Surrogate Functions with Random Bases, Statistics and Computing, 27, 1473-1490.

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

Our new abstract will be presented at SfN: Fortin N.J., Elias G.A., Li L., Agostinelli F., Baldi P., Shahbaba B. (2018), Sequential activation of upcoming nonspatial item representations in hippocampal activity, Society for Neuroscience Abstract (San Diego, CA).

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.

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.

(949) 824-0623

2224 DBH, UC Irvine, CA 92697

babaks at uci dot edu

Contact