Shahbaba, B., Li, L., Agostinelli, F., Saraf, M., Cooper, K.W., Haghverdian, D., Elias, G.A., Baldi, P., and Fortin, N.J. (2022), Hippocampal ensembles represent sequential relationships among discrete nonspatial events,
Nature Communications: online.
LLan, S., Li, S., and Shahbaba, B. (2022), Scaling Up Bayesian Uncertainty Quantification for Inverse Problems Using Deep Neural Networks, SIAM/ASA Journal on Uncertainty Quantification, 10:4, 1684-1713.
Denti, F., Azevedo, R., Lo, C., Wheeler, D., Gandhi, S,P., Guindani, M., Shahbaba, B. (2023), A Horseshoe Pit mixture model for Bayesian screening with an application to light sheet fluorescence microscopy in brain imaging, Annals of Applied Statistics (to appear)
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.
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 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.
See my GitHub page for updates on our NSF and NIH funded projects.
SoCal Data Science -- a very successful first year
Information on our new NSF grant from the Harnessing the Data Revolution (HDR) program can be found here.
Information on our NIH-funded Irvine Summer Institute in Biostatistics and Undergraduate Data Science can be found here.
Information on our NIH-R01 grant on Scalable Bayesian Stochastic Process Models for Neural Data Analysis can be found here.
We will have our first NeuroDataScience workshop on Sep 22-23, 2022. This is a 2-day workshop to highlight recent challenges and developments related to big data problems in neuroscience.
2222 ISEB, UC Irvine, CA 92697
babaks at uci dot edu