HIGHLIGHTS

Research  

Ren, Y., Shahbaba, B., and Stark, C. (2023), Improving clinical efficiency in screening for cognitive impairment due to Alzheimer’s, Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring (to appear).

Tran, B., Shahbaba, B., Mandt, S., Filippone, M. (2023), Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes, ICML 2023 (to appear).

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.

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)

Teaching  

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.

News  

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.