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
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.
2224 DBH, UC Irvine, CA 92697
babaks at uci dot edu