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



Shahbaba, B., Li, L., Agostinelli, F., Saraf, M., Cooper, K.W., Haghverdian, D., Elias, G.A., Baldi, P., and Fortin, N.J. (2021), Hippocampal ensembles represent sequential relationships among discrete nonspatial events,

Nature Communications: online.

Lan, S., Li, S., and Shahbaba, B. (2022), Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Network, SIAM/ASA Journal on Uncertainty Quantification (to appear).

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., 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).


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

SoCal Data Science (NSF supported data science training program).

ISI-BUDS (NIH-supported summer school on biostatistics).

COSMOS data science summer program for high school students.

Data Academy 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 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.


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.

Information on our new NSF grant from the Harnessing the Data Revolution (HDR) program can be found here.

Information on our MODULUS grant for data-driven mechanistic modeling can be found here.

Information on our NIH-R01 grant can be found here.

I will give a talk at LLNL on January 20, 2022.

I will give a talk at ASU on October 1, 2021.

We are organizing a workshop on Big Data Challenges in Neuroscience. The workshop is supported by the NSF-Simons Center at UCI and is organized by the UCI Data Science Initiative.

Andrew Holbrook has joined UCLA Biostat as an Assistant Professor.

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(949) 824-0623

2224 DBH, UC Irvine, CA 92697

babaks at uci dot edu