Babak Shahbaba


Babak Shahbaba, PhD

Professor of Statistics and Computer Science

Director of The UCI Data Science Initiative

University of California, Irvine

Scalable Bayesian Inferences

Nonparametric Bayesian Methods

Statistical Methods in Biological Sciences

(949) 824-0623

2222 ISEB, UC Irvine, CA 92697

babaks at uci dot edu




Lan, S. and Shahbaba, B. (2016), Sampling Constrained Probability Distributions using Spherical Augmentation, in "Algorithmic Advances in Riemannian Geometry and Applications" (Eds., Minh, H. Q. and Murino, V.), Springer.

Shahbaba, B., Behseta, S., and Vandenberg-Rodes, A. (2015), Neuronal Spike Train Analysis Using Gaussian Process Models, in "Nonparametric Bayesian Methods in Biostatistics and Bioinformatics" (Eds., Mitra, R. and Muller, P.), Springer.

Shahbaba, B. (2012), Biostatistics with R, An Introduction to Statistics Through Biological Data, Springer.


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).

Denti, F., Azevedo, R., Lo, C., Wheeler, D. G. , Gandhi, S. P. , Guindani, M., and Shahbaba, B. “A horseshoe

mixture model for Bayesian screening with an application to light sheet fluorescence microscopy in

brain imaging,” The Annals of Applied Statistics, vol. 17, no. 3, pp. 2639 – 2658, 2023.

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.

Lan, 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.

Louis Ehwerhemuepha, Bradley Roth, Anita Patel, Olivia Heutlinger, Carly Heffernan, Antonio Arrieta, Terence Sanger, Dan Cooper, Babak Shahbaba, Anthony Chang, William Feaster, Sharief Taraman, Hiroki Morizono, Rachel Marano (2022), Analysis of COVID-19 Disease Severity Among US Children with Congenital and Acquired Cardiovascular Conditions , JAMA Network Open (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 Royal Society Interface (to appear).

Granados-Garciaa, G., Fiecas, M., Shahbaba, B., Fortinc, N.J., Ombao, H. (2021), Brain Waves Analysis Via a Non-Parametric Bayesian Mixture of Autoregressive Kernels, Computa- tional Statistics and Data Analysis (to appear).

Cramer S.C., See, J., Liu, B, Edwardson, M, Ximing, W., Radom-Aizik, S., Haddad, F., Shahbaba, B., Wold, S.L. Dromerick, A.W., Winstein, C.J., Genetic Factors, Brain Atrophy, and Response to Rehabilitation Therapy after Stroke, Neurorehabilitation and Neural Repair (to appear).

Masouleh, S., Holsclaw, T., Shahbaba, B., and Gillen, D. (2021) A Flexible Joint Longitudinal-Survival Model for Analyzing Longitudinally Sampled Biomarkers. Open Journal of Statistics, 11, 778-805.

Shahbaba, B., Lan, S., Streets, J. D., and Holbrook, A. J. (2020), Nonparametric Fisher Geometry with Application to Density Estimation, Uncertainty in Artificial Intelligence (UAI 2020).

Erani et al. (2020+), EEG Improves Diagnosis of Acute Stroke and Large Vessel Occlusion, Stroke (to appear).

Frostig, R., Zhu, J., Hancock, A., Qi, L., Telkmann, K., Shahbaba, S., and Chen, A. (2019) Spatiotemporal dynamics of pial collateral blood flow following permanent MCA occlusion in a rat model of sensory-based protection: a Doppler OCT study, Neurophotonics, 6(4), 045012.

Shahbaba, B., Lomeli, L. M., Chen, T, Lan, S. (2019), Deep Markov Chain Monte Carlo,

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, Vancouver.

Li, L., Holbrook, A., Shahbaba, B, Baldi, P. (2019), Neural Network Gradient Hamiltonian Monte Carlo, Computational Statistics, 34(1), 281-299.

Baldi, P. and Shahbaba, B. (2019+), Bayesian Causality, The American Statistician (to appear).

Gao, X,, Shen, W., Shahbaba, B., Fortin, N.J., Ombao, H. (2019+), Evolutionary State-Space Model and Its Application to Time-Frequency Analysis of Local Field Potentials, Statistica Sinica (to appear).

Zhang, C., Shahbaba, B., Zhao, H. (2018), Variational Hamiltonian Monte Carlo via Score Matching, Bayesian Analysis, 13(2), 485-506.

Holbrook, A., Lan, S., Vandenberg-Rodes, A., and Shahbaba, B. (2017) Geodesic Lagrangian Monte Carlo over the space of positive definite matrices: with application to Bayesian spectral density estimation, Journal of Statistical Computation and Simulation, 88(5), 982-1002.

Holbrook, A., Vandenberg-Rodes, A., Fortin, N., Shahbaba, B. (2017), A Bayesian supervised dual-dimensionality reduction model for simultaneous decoding of LFP and spike train signals, Stat, 6 (1), 53-67.

Zhang, C., Shahbaba, B., Zhao, H. (2017), Hamiltonian Monte Carlo Acceleration Using Surrogate Functions with Random Bases, Statistics and Computing, 27, 1473-1490.

Gao, X., Shahbaba, B., and Ombao, H. (2017) Modeling Binary Time Series Using Gaussian Processes With Application to Predicting Sleep States, Journal of Classification (to appear).

Shahbaba, B. (2017), Review of ``Handbook of Discrete-Valued Time Series,'' edited by Davis, R.A., Holan, S.H., Lund, R., Ravishanker, N., Journal of the American Statistical Association, 112(520), 1771.

Shahbaba, B. (2017), Review of ``Fundamentals of Statistical Experimental Design and Analysis,'' by Robert G. Easterling, The American Statistician, 71(4), 369.

Albitar, M., Ma, W., Lund, L., Shahbaba, B., Uchio, E., Feddersen, S., Moylan, D., Wojno, K., and Shore, N. (2017), Prostatectomy-based validation of combined urine and plasma test for predicting high grade prostate cancer, The Prostate, 78(4):294-299.

Albitar, M., Ma, W., Lund, L., Shahbaba, B., Uchio, E., Feddersen, S., Moylan, D., Wojno, K., and Shore, N. (2017), A Multi-Center Prospective Study to Validate an Algorithm Using Urine and Plasma Biomarkers for Predicting Gleason $\ge3+4$ Prostate Cancer on Biopsy, Journal of Cancer, 8(13), 2554-2560.

Zhang, C., Shahbaba, B., Zhao, H. (2017), Precomputing strategy for Hamiltonian Monte Carlo Method based on regularity in parameter space, Computational Statistics, 32(1), 253-279.

Vandenberg-Rodes, A., Moftakhari, H. R., AghaKouchak, A., Shahbaba, B., Sanders, B. F. and Matthew, R. A. (2016), Projecting nuisance flooding in a warming climate using generalized linear models and Gaussian processes, J. Geophys. Res. (Oceans), Accepted Author Manuscript. doi:10.1002/2016JC012084.

Zhou, B., Moorman, D. E., Behseta, S., Ombao, H., and Shahbaba, B. (2016), A Dynamic Bayesian Model for Characterizing Cross-Neuronal Interactions During Decision Making, Journal of the American Statistical Association, 111 (514), 459-471.

Agostinelli, F., Ceglia, N., Shahbaba, B., Sassone-Corsi, P., Baldi, P., What Time is it? Deep Learning Approaches for Circadian Rhythms (2016), Bioinformatics, 32(12), i8-i17.

Shahbaba, B. (2016), Review of "Geometry Driven Statistics," edited by Dryden I.L. ad Kent, J.T., Journal of the American Statistical Association, 111(516), 1840.

Lan, S., Palacios, J., Karcher, M., Minin, V., Shahbaba, B. (2015) An Efficient Bayesian Inference Framework for Coalescent-Based Nonparametric Phylodynamics, Bioinformatics, 31(20), 3282-3289.

Moog, N.K,, Buss, C., Entringer, S., Shahbaba, V., Gillen, D., Hobel, C.J., and Wadhwa, P.D. (2016), Maternal exposure to childhood trauma is associated during pregnancy with placental-fetal stress physiology, Biological Psychiatry, 79(10):831-9.

Lan, S., Stathopoulos, V., Shahbaba, B., and Girolami, M. (2015), Markov Chain Monte Carlo from Lagrangian Dynamics (2015), Journal of Computational and Graphical Statistics, 24(2), 357-378.

Vandenberg-Rodes, A. and Shahbaba, B. (2015), Dependent Matérn Processes for Multivariate Time Series, , arXiv:1502.03466.

Quinlan, E.B., Dodakian, L., See, J., McKenzie, A., Le, V., Wojnowicz, M., Shahbaba, B., Cramer, S.C. (2015), Neural function, injury, and stroke subtype predict treatment gains after stroke, Annals of Neurology, 77(1), 132-45.

Shahbaba, B. (2015), Review of "Analysis of Neural Data,'' by Kass, R.E., Eden, U., and Brown, E., Journal of the American Statistical Association, 110(510), 578.

Shahbaba, B. (2015), Review of "Applied Statistical Inference: Likelihood and Bayes,'' by Held, L. and Sabanés Bové, D., Journal of the American Statistical Association, 110(510), 579.

Shahbaba, B., Comment on “Robust Bayesian Graphical Modeling Using Dirichlet t-distribution,” Bayesian Analysis, 9(3), 557-560.

Lan, S., Zhou, B., and Shahbaba, B. Spherical Hamiltonian Monte Carlo for Constrained Target Distributions, ICML 2014.

Ahn, S., Shahbaba, B., and Welling, M., Distributed Stochastic Gradient MCMC, ICML 2014.

Shahbaba, B., Lan, S., Streets, J., Comment on “Geodesic Monte Carlo on Embedded Manifolds,” Scandinavian Journal of Statistics, 41(1), 14-15.

Lan, S., Streets, J., and Shahbaba, B. Wormhole Hamiltonian Monte Carlo, AAAI 2014.

Shahbaba, B., Zhou, B., Lan, S., Ombao, H., Moorman, D., and Behseta, S., A Semiparametric Bayesian Model for Detecting Synchrony Among Multiple Neurons, Neural Computation, 26(9), 2025-51.

Shahbaba, B., Lan, S., Johnson, W.O. , Neal, R.M.,  Split Hamiltonian Monte Carlo, Statistics and Computing, 24(3), 339-349.

Pearson-Fuhrhop, K.M., Minton, B., Acevedo, D., Shahbaba, B., and Cramer, S.C. (2013), Genetic variation in the human brain dopamine system influences motor learning and its modulation by L-Dopa, PLOS ONE, 8(4):e61197. doi: 10.1371.

Shahbaba, B., Johnson, W.O. (2013), Bayesian Nonparametric Variable Selection as an Exploratory Tool for Discovering Differentially Expressed Genes, Statistics in Medicine, 30(12), 2114-26.

Buss, C., Davis, E.P., Shahbaba, B., Pruessner, J.C., Head, K., and Sandman C.A. (2012), Maternal cortisol over the course of pregnancy and subsequent child amygdala and hippocampus volumes and affective problems, PNAS, 109(20):E1312–9.

Zhou, B., Tieu, K.H., Konstorum, A., Duong, T., Wells, WM, Brown, G.G., Stern, H., and Shahbaba, B. (2013), A hierarchical modeling approach to data analysis and study design in a multi-site experimental fMRI study, Psychometika, 78(12), 260-278.

Shahbaba, B, Shachaf, CM, and Yu, Z (2012), A pathway analysis method for genome-wide association studies, Statistics in Medicine, 31(10), 988-1000.

Shahbaba, B, Yu, Y, and van Dyk, DA (2011), Comment on "Data Augmentation for Support Vector Machines'', Bayesian Analysis, 6(1), 31-36.

Shahbaba B, Tibshirani R, Shachaf, CM, and Plevritis SK (2011), Bayesian gene set analysis for identifying significant biological

pathways, Journal of the Royal Statistical Society, Series C, Volume 60, Issue 4, 541-557.  

Shahbaba B, Neal RM (2009), Nonlinear models using Dirichlet process mixtures, Journal of Machine Learning Research, Volume 10, 1829-1850.

Shahbaba B, Gentles AJ, Beyene J, Plevritis SK, Greenwood CMT (2009), A Bayesian nonparametric method for model evaluation: Application to genetic studies, Journal of Nonparametric Statistics, Volume 21, Issue 3, 379 - 396.

Gentles AJ, Alizadeh AA, Lee SI, Myklebust JH, Shachaf CM, Shahbaba B, Levy R, Koller D, Plevritis SK (2009), A pluripotency signature predicts histological transformation and influences survival in follicular lymphoma patients, Blood, 114(15), 3158 - 3166.

Shahbaba, B (2009), Discovering hidden structures using mixture models: Application to nonlinear time series processes, Studies in Nonlinear Dynamics & Econometrics, Vol. 13, No. 2, Article 5.

Shahbaba B, Neal RM (2007) Improving classification when a class hierarchy is available using a hierarchy-based prior, Bayesian Analysis, 2(1), 221-238.

Shahbaba B, Neal RM (2006), Gene function classification using Bayesian models with hierarchy-based priors, BMC Bioinformatics, 7:447.