Lan, S., Holbrook, A., Elias, G.A., Fortin, N.J., Ombao, H., and Shahbaba, B. (2019), Flexible Bayesian Dynamic Modeling of Correlation and Covariance Matrices, Bayesian Analysis (to appear).
Li, L., Pluta, D., Shahbaba, B., Fortin, N., Ombao, H., Baldi, P. (2019), Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes, NeurIPS 2019.
Baldi, P. and Shahbaba, B. (2019), Bayesian Causality, The American Statistician (to appear).
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).
Zhang, C., Shahbaba, B., Zhao, H. (2018), Variational Hamiltonian Monte Carlo via Score Matching, Bayesian Analysis, 13(2), 485-506.
Zhang, C., Shahbaba, B., Zhao, H. (2017), Hamiltonian Monte Carlo Acceleration Using Surrogate Functions with Random Bases, Statistics and Computing, 27, 1473-1490.
See my GitHub page for most up-to-date lecture notes, codes, and talks.
STATS 235, Statistical Machine Learning (Spring 2019): Overview of some general concepts in statistics and machine learning; Overview of optimization and sampling algorithms; Supervised and unsupervised learning; Regularization; Splines; Gaussian process regression models; SVM; Tree-based methods; Graphical models; Neural networks; Deep learning
STATS 230, Statistical Computing Methods (Fall 2019): Numerical linear algebra; Optimization methods; Sampling algorithms; EM; Bootstrap
STATS 225, Bayesian Data Analysis (Winter 2020): The objective of this course is to explore Bayesian statistical methods and discuss their applications in real life problems. Students will also learn several computational techniques commonly used in Bayesian analysis.
STATS 275, Statistical Consulting (Winter 2020): In this course, students work on real scientific projects in collaboration with UCI researchers. The objective of this course is to learn how to form a scientific question, apply appropriate statistical methods for inference, and properly communicate findings to the scientific community.
See my GitHub page for updates on our NSF and NIH funded projects.
See the annoucement for our new grant.
I am representing ISBA on the Program Committee for JSM 2020.
I am attending the 2019 Data Science Institute Workshop to represent UCI on a UC-wide data science task force.
I am co-organizaing (with Roman Vershynin and Qing Nie) a symposium on Mathematical Challenges in Biological Big Data, March 16-17, 2020. It is sponsered by CMCF.
Shiwei Lan is joining ASU as an Assistant Professor of Statistics.
Cheng Zhang is joining Peking University as an Assistant Professor.
Andrew Holbrook wes selected as the runner-up for the 2018 Savage Award.
I am giving a talk at ICERM's workshop on “Computational Statistics and Data-Driven Models", April 20-24, 2020.
I am attending the 2019 Simons Conference on Theory & Biology, April 11-12, 2019 at the Simons Foundation.
2224 DBH, UC Irvine, CA 92697
babaks at uci dot edu