PUBLICATIONS

Selected Papers

Zhou, W., Qu, A., Cooper, K., Fortin, N., Shahbaba, B. (2024), A Model-Agnostic Graph Neural Network (MaGNET) for Integrat- ing Local and Global Information, Journal of the American Statistical Association (to appear).

Duan J, Ngo MN, Karri SS, Tsoi LC, Gudjonsson JE, Shahbaba* B, Lowengrub* J, Andersen* B. (2024), tauFisher accurately predicts circadian time from a single sample of bulk and single-cell transcriptomic data, Nature Communications (to appear, *corresponding authors).

Sutter, T.M., Meng, Y., Fortin, N., Vogt, J.E., Shahbaba, B., Mandt, S. (2024), Unity by Diversity: Improved Representation Learning in Multimodal VAEs, Neural Information Processing Systems (NeurIPS 38).

Tran, B., Shahbaba, B., Mandt, S., Filippone, M. (2023), Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes, The 40th International Conference on Machine Learning (ICML).

Denti, F., Azevedo, R., Gandhi, S.P., Guindani, M., Shahbaba, B. (2023), Horseshoe Pit – A Unified Framework for Large-Scale Bayesian Inference with Application to Whole Brain Imaging, Annals of Applied Statistics (to appear).

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 10 (4), 1684-1713.

Ehwerhemuepha, L., Roth, B., Patel, A., Heutlinger, O., Heffernan, C., Arrieta, A., Sanger, T., Cooper, D., Shahbaba, B., Chang, A., Feaster, W., Taraman, S., Morizono, H., Marano, R. (2022), Analysis of COVID-19 Disease Severity Among US Children with Congenital and Acquired Cardiovascular Conditions, JAMA Network Open (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), Hip- pocampal ensembles represent sequential relationships among discrete nonspatial events, Nature Communications, 13, 787.

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).

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.

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

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

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

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.

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.

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

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.

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, 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.

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