Babak Shahbaba


Babak Shahbaba, PhD

Professor and Chancellor's Fellow

Director of The UCI Data Science Initiative

Departments of Statistics and Computer Science

University of California, Irvine

Scalable Bayesian Inferences

Nonparametric Bayesian Methods

Statistical Methods in Biological Sciences



Denti, F., Azevedo, R., Lo, C., Wheeler, D., Gandhi, S,P., Guindani, M., Shahbaba, B. (2021), A Horseshoe Pit mixture model for Bayesian screening with an application to light sheet fluorescence microscopy in brain imaging: arXiv.

Shahbaba, B., Li, L., Agostinelli, F., Saraf, M., Elias, G.A., Baldi, P., and Fortin, N.J., Hippocampal ensembles represent sequential relationships among discrete nonspatial events: bioRxiv.

Lan, S., Li, S., and Shahbaba, B. (2021), Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks: arXiv.

Shahbaba, B., Lan, S., Streets, J. D., and Holbrook, A. J. (2020), Nonparametric Fisher Geometry with Application to Density Estimation, Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR volume 124, 2020.

Martinez Lomeli, L., Iniguez, A., Shahbaba, B., Lowengrub, J. S., and Minin, V. (2021), Optimal Experimental Design for Mathematical Models of Hematopoiesis, Journal of the Royal Society Interface (to appear).

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).


See my GitHub page for most up-to-date lecture notes, codes, and talks.

COSMOS data science summer program for high school students.

STATS 235, Statistical Machine Learning: 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: Numerical linear algebra; Optimization methods; Sampling algorithms; EM; Bootstrap

STATS 225, Bayesian Data Analysis: 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: 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 our upcoming courses and wokshops through the UCI Data Science Initiative.

Derenik Haghverdian has receieved the prestigious NSF Graduate Research Fellowship.

Andrew Holbrook has joined UCLA Biostat as an Assistant Professor.

Andrew Holbrook wes selected as the runner-up for the 2018 Savage Award.

I am co-organizing (with Roman Vershynin and Qing Nie) a symposium on Mathematical Challenges in Biological Big Data, March 16-17, 2020. It is sponsered by CMCF.

See the annoucement for our new grant.

I am representing ISBA on the Program Committee for JSM 2020.

I am giving a talk at ICERM's workshop on “Computational Statistics and Data-Driven Models", April 20-24, 2020.

Shiwei Lan has joined ASU as an Assistant Professor of Statistics.

Cheng Zhang has joined Peking University as an Assistant Professor.

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(949) 824-0623

2224 DBH, UC Irvine, CA 92697

babaks at uci dot edu