Publications

My work has appeared in statistics and machine-learning venues including the Journal of the Royal Statistical Society, Series B, the Journal of the American Statistical Association, the Annals of Statistics, the Journal of Machine Learning Research, Transactions on Machine Learning Research, NeurIPS, ICML, UAI, and AAAI, as well as scientific journals such as Nature Communications, PNAS, Stroke, Blood, and Alzheimer’s & Dementia.

Full, continuously updated list on ORCID and Google Scholar. Names marked * denote corresponding authors.

Book

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

Preprints & under review

Moslemi, Z., Liang, Z., Fortin, N., & Shahbaba, B. Heterogeneous Graph Alignment for Joint Reasoning and Interpretability. Under review. arXiv:2601.22593
Liang, Z., Poursiami, H., Yang, Z., Cooper, K., Jaiswal, A., Parsa, M., Fortin, N., & Shahbaba, B. (2026). ConformalHDC: Uncertainty-Aware Hyperdimensional Computing with Application to Neural Decoding. Under review. arXiv:2602.21446

Peer-reviewed articles & proceedings

Liang, Z., Qu, A., & Shahbaba, B. (2026). Meta Fusion: A Unified Framework for Multimodality Fusion with Mutual Learning. Journal of the Royal Statistical Society, Series B (to appear).
Cooper, K., Shahbaba, B., & Fortin, N. (2026). Neurodatascience: past, present, and future. Data Science in Science, 5(1).
Sciscione, A. C., Gyamfi-Bannerman, C., Walker, M., Shahbaba, B., Godt, J., & Iriye, B. K. (2026). Biomarker screen-guided care for preterm birth risk in nulliparous pregnancies: a subgroup analysis of the PRIME randomized controlled trial. The Journal of Maternal-Fetal & Neonatal Medicine, 39(1).
Iriye, B. K., O’Brien, J. M., Ennen, C. S., Barrilleaux, P. S., Berkin, J. A., Palatnik, A., Son, M., Gyamfi-Bannerman, C., McDonnold, M., Markenson, G. R., Sciscione, A. C., Biggio, J. R., Wolf, S., Sullivan, S. A., Walker, M., Shahbaba, B., Godt, J., & Pound, S. P. (2026). Neonatal impact of maternal biomarker screening for risk of preterm birth with targeted interventions (PRIME): a multicenter, randomized, controlled trial. Pregnancy, 2, e70202.
Miao, R., Shahbaba, B., & Qu, A. (2025). Reinforcement Learning for Individual Optimal Policy from Heterogeneous Data. Annals of Statistics, 53(4), 1513–1534.
Yuan, Y., Zhang, Y., Shahbaba, B., Fortin, N., Cooper, K., Nie, Q., & Qu, A. (2025). Optimal Transport based Cross-Domain Integration for Heterogeneous Data. Journal of the American Statistical Association, 120(551), 1449–1462.
Poursiami, H., Moshruba, A., Cooper, K. W., Gobin, D., Kaiser, M. A., Singh, A., Noor, R., Shahbaba, B., Jaiswal, A., Fortin, N. J., & Parsa, M. (2025). A scalable reinforcement learning framework inspired by hippocampal memory mechanisms for efficient contextual and sequential decision making. Scientific Reports, 15, 25221.
Moslemi, Z., Liang, Z., Fortin, N., & Shahbaba, B. (2025). Multi-Graph Meta-Transformer: An Interpretable Framework for Cross-Graph Functional Alignment in Neural Decoding. NeurIPS 2025 AI for Science (spotlight; runner-up, best poster award).
Ren, Y., Shahbaba, B., & Stark, C. (2025). Identifying dementia neuropathology using low-burden clinical data. Alzheimer’s & Dementia, 21(8), e70539.
Schetzsle, B., Lee, J., Bornstein, A., Shahbaba, B., & Guindani, M. (2025). A Bayesian Time-Varying Psychophysiological Interaction Model. Data Science in Science, 4(1), 2519436.
Zhou, W., Qu, A., Cooper, K., Fortin, N., & Shahbaba, B. (2024). A Model-Agnostic Graph Neural Network (MaGNET) for Integrating Local and Global Information. Journal of the American Statistical Association, 120(550), 1225–1238.
Moslemi, Z., Meng, Y., Lan, S., & Shahbaba, B. (2024). Scaling Up Bayesian Neural Networks with Neural Networks. Transactions on Machine Learning Research.
Duan, J., Ngo, M. N., Karri, S. S., Tsoi, L. C., Gudjonsson, J. E., 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, 15, 3840.
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).
Cramer, S. C., Parodi, L., Moslemi, Z., Braun, R., Aldridge, C., Shahbaba, B., Rosand, J., & Holman, A. (2024). Genetic Variation and Stroke Recovery: the STRONG Study. Stroke, 55(8), 2094–2102.
Hoffman, M. K., Kitto, C., Zhang, Z., Shi, J., Walker, M. G., Shahbaba, B., & Ruhstaller, K. (2024). Neonatal Outcomes after Maternal Biomarker-Guided Preterm Birth Intervention: The Avert Preterm Trial. Diagnostics, 14(14), 1462.
Tran, B., Shahbaba, B., Mandt, S., & Filippone, M. (2023). Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes. International Conference on Machine Learning (ICML 40).
Ren, Y., Shahbaba, B., & Stark, C. (2023). Improving clinical efficiency in screening for cognitive impairment due to Alzheimer’s. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 15(4), e12494.
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, 17(3), 2639–2658.
Lan, S., Li, S., & 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, 5(5), e2211967.
Shahbaba, B., Li, L., Agostinelli, F., Saraf, M., Cooper, K. W., Haghverdian, D., Elias, G. A., Baldi, P., & Fortin, N. J. (2022). Hippocampal ensembles represent sequential relationships among discrete nonspatial events. Nature Communications, 13, 787.
Martinez Lomeli, L., Iniguez, A., Shahbaba, B.*, Lowengrub, J. S.*, & Minin, V.* (2021). Optimal Experimental Design for Mathematical Models of Hematopoiesis. Journal of the Royal Society Interface, 18(174).
Cramer, S. C., See, J., Liu, B., Edwardson, M., Wang, X., Radom-Aizik, S., Haddad, F., Shahbaba, B., Wolf, S. L., Dromerick, A. W., & Winstein, C. J. (2021). Genetic Factors, Brain Atrophy, and Response to Rehabilitation Therapy after Stroke. Neurorehabilitation and Neural Repair, 36(2), 131–139.
Granados-Garcia, G., Fiecas, M., Shahbaba, B., Fortin, N. J., & Ombao, H. (2021). Brain Waves Analysis Via a Non-Parametric Bayesian Mixture of Autoregressive Kernels. Computational Statistics and Data Analysis, 174, 107409.
Masouleh, S., Holsclaw, T., Shahbaba, B., & 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., & Holbrook, A. J. (2020). Nonparametric Fisher Geometry with Application to Density Estimation. Uncertainty in Artificial Intelligence (UAI 2020).
Gao, X., Shen, W., Shahbaba, B., Fortin, N. J., & Ombao, H. (2020). Evolutionary State-Space Model and Its Application to Time-Frequency Analysis of Local Field Potentials. Statistica Sinica, 30, 1561–1582.
Young, K. H., et al. (2020). A refined cell-of-origin classifier with targeted NGS and Artificial Intelligence shows robust predictive value in DLBCL. Blood Advances, 4(14), 3391–3404.
Erani, F., Zolotova, N., Vanderschelden, B., Khoshab, N., Sarian, H., Nazarzai, L., Wu, J., Chakravarthy, B., Hoonpongsimanont, W., Yu, W., Shahbaba, B., Srinivasan, R., & Cramer, S. C. (2020). Electroencephalography Might Improve Diagnosis of Acute Stroke and Large Vessel Occlusion. Stroke, 51(11), 3361–3365.
Frostig, R., Zhu, J., Hancock, A., Qi, L., Telkmann, K., Shahbaba, S., & 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.
Li, L., Pluta, D., Shahbaba, B., Fortin, N., Ombao, H., & Baldi, P. (2019). Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes. Neural Information Processing Systems (NeurIPS 2019).
Lan, S., Holbrook, A., Elias, G. A., Fortin, N. J., Ombao, H., & Shahbaba, B. (2019). Flexible Bayesian Dynamic Modeling of Correlation and Covariance Matrices. Bayesian Analysis, 15(4), 1199–1228.
Li, L., Holbrook, A., Shahbaba, B., & Baldi, P. (2019). Neural Network Gradient Hamiltonian Monte Carlo. Computational Statistics, 34(1), 281–299.
Baldi, P., & Shahbaba, B. (2019). Bayesian Causality. The American Statistician, 74(3), 249–257.
Gao, X., Shahbaba, B., & Ombao, H. (2018). Modeling Binary Time Series Using Gaussian Processes With Application to Predicting Sleep States. Journal of Classification, 35(3), 549–579.
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., & Shahbaba, B. (2018). 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.
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.
Albitar, M., Ma, W., Lund, L., Shahbaba, B., Uchio, E., Feddersen, S., Moylan, D., Wojno, K., & Shore, N. (2017). Prostatectomy-based validation of combined urine and plasma test for predicting high grade prostate cancer. Prostate, 78(4), 294–299.
Albitar, M., Ma, W., Lund, L., Shahbaba, B., Uchio, E., Feddersen, S., Moylan, D., Wojno, K., & Shore, N. (2017). A Multi-Center Prospective Study to Validate an Algorithm Using Urine and Plasma Biomarkers for Predicting Gleason ≥ 3+4 Prostate Cancer on Biopsy. Journal of Cancer, 8(13), 2554–2560.
Vandenberg-Rodes, A., Moftakhari, H. R., AghaKouchak, A., Shahbaba, B., Sanders, B. F., & Matthew, R. A. (2016). Projecting nuisance flooding in a warming climate using generalized linear models and Gaussian processes. Journal of Geophysical Research: Oceans, 121(11), 8008–8020.
Zhou, B., Moorman, D. E., Behseta, S., Ombao, H., & 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. (2016). What Time is it? Deep Learning Approaches for Circadian Rhythms. Bioinformatics, 32(12), i8–i17.
Lan, S., & Shahbaba, B. (2016). Sampling Constrained Probability Distributions using Spherical Augmentation. In Algorithmic Advances in Riemannian Geometry and Applications (Minh, H. Q. & Murino, V., Eds.). Springer.
Moog, N. K., Buss, C., Entringer, S., Shahbaba, B., Gillen, D., Hobel, C. J., & Wadhwa, P. D. (2016). Maternal exposure to childhood trauma is associated during pregnancy with placental-fetal stress physiology. Biological Psychiatry, 79, 831–839.
Shahbaba, B., Behseta, S., & Vandenberg-Rodes, A. (2015). Neuronal Spike Train Analysis Using Gaussian Process Models. In Nonparametric Bayesian Inference in Biostatistics (Mitra, R. & Müller, P., Eds.). Springer.
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.
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), 132–145.
Entringer, S., Epel, E., Lin, J., Blackburn, E., Buss, C., Shahbaba, B., Gillen, D., Venkataramanan, R., Simhan, H., & Wadhwa, P. (2015). Maternal folate concentration in early pregnancy and newborn telomere length. Annals of Nutrition and Metabolism, 66(4), 202–208.
Lan, S., Zhou, B., & Shahbaba, B. (2014). Spherical Hamiltonian Monte Carlo for Constrained Target Distributions. International Conference on Machine Learning (ICML 31).
Ahn, S., Shahbaba, B., & Welling, M. (2014). Distributed Stochastic Gradient MCMC. International Conference on Machine Learning (ICML 31).
Shahbaba, B., Lan, S., Johnson, W. O., & Neal, R. M. (2014). Split Hamiltonian Monte Carlo. Statistics and Computing, 24(3), 339–349.
Lan, S., Stathopoulos, V., Shahbaba, B., & Girolami, M. (2015). Markov Chain Monte Carlo from Lagrangian Dynamics. Journal of Computational and Graphical Statistics, 24(2), 357–378.
Shahbaba, B., Zhou, B., Lan, S., Ombao, H., Moorman, D., & Behseta, S. (2014). A Semiparametric Bayesian Model for Detecting Synchrony Among Multiple Neurons. Neural Computation, 26(9), 2025–2051.
Lan, S., Streets, J., & Shahbaba, B. (2014). Wormhole Hamiltonian Monte Carlo. AAAI Conference on Artificial Intelligence.
Shahbaba, B., & Johnson, W. O. (2013). Bayesian Nonparametric Variable Selection as an Exploratory Tool for Discovering Differentially Expressed Genes. Statistics in Medicine, 30(12), 2114–2126.
Zhou, B., Tieu, K. H., Konstorum, A., Duong, T., Wells, W. M., Brown, G. G., Stern, H. S., & Shahbaba, B. (2013). A hierarchical modeling approach to data analysis and study design in a multi-site experimental fMRI study. Psychometrika, 78(2), 260–278.
Pearson-Fuhrhop, K. M., Minton, B., Acevedo, D., Shahbaba, B., & 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).
Buss, C., Davis, E. P., Shahbaba, B., Pruessner, J. C., Head, K., & 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–E1319.
Shahbaba, B., Shachaf, C. M., & Yu, Z. (2012). A pathway analysis method for genome-wide association studies. Statistics in Medicine, 31(10), 988–1000.
Entringer, S., Epel, E. S., Lin, J., Buss, C., Shahbaba, B., Blackburn, E. H., Simhan, H. N., & Wadhwa, P. D. (2012). Maternal psychosocial stress during pregnancy is associated with newborn leukocyte telomere length. American Journal of Obstetrics and Gynecology, 208(2), 134.e1–7.
Shahbaba, B., Tibshirani, R., Shachaf, C. M., & Plevritis, S. K. (2011). Bayesian gene set analysis for identifying significant biological pathways. Journal of the Royal Statistical Society, Series C, 60(4), 541–557.
Shahbaba, B., Gentles, A. J., Beyene, J., Plevritis, S. K., & Greenwood, C. M. T. (2009). A Bayesian nonparametric method for model evaluation: application to genetic studies. Journal of Nonparametric Statistics, 21(3), 379–396.
Gentles, A. J., Alizadeh, A. A., Lee, S. I., Myklebust, J. H., Shachaf, C. M., Shahbaba, B., Levy, R., Koller, D., & Plevritis, S. K. (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, 13(2), Article 5.
Shahbaba, B., & Neal, R. M. (2009). Nonlinear models using Dirichlet process mixtures. Journal of Machine Learning Research, 10, 1829–1850.
Shahbaba, B., & Neal, R. M. (2007). Improving classification when a class hierarchy is available using a hierarchy-based prior. Bayesian Analysis, 2(1), 221–238.
Shahbaba, B., & Neal, R. M. (2006). Gene function classification using Bayesian models with hierarchy-based priors. BMC Bioinformatics, 7, 447.

Selected reviews & commentaries

Shahbaba, B. (2017). Review of Handbook of Discrete-Valued Time Series (Davis, R. A., Holan, S. H., Lund, R., & Ravishanker, N., Eds.). Journal of the American Statistical Association, 112(520), 1771–1783.
Shahbaba, B. (2017). Review of Fundamentals of Statistical Experimental Design and Analysis (Easterling, R. G.). The American Statistician, 71(4), 369–372.
Shahbaba, B. (2016). Review of Geometry Driven Statistics (Dryden, I. L. & Kent, J. T., Eds.). Journal of the American Statistical Association, 111(516), 1840–1851.
Shahbaba, B. (2015). Review of Analysis of Neural Data (Kass, R. E., Eden, U., & Brown, E.). Journal of the American Statistical Association, 110(510), 578.
Shahbaba, B. (2015). Review of Applied Statistical Inference: Likelihood and Bayes (Held, L. & Sabanés Bové, D.). Journal of the American Statistical Association, 110(510), 579.
Shahbaba, B., Lan, S., & Streets, J. (2014). Contribution to the discussion of “Geodesic Monte Carlo on Embedded Manifolds.” Scandinavian Journal of Statistics, 41(1), 14–15.
Shahbaba, B. (2014). Comment on “Robust Bayesian Graphical Modeling Using Dirichlet t-distribution.” Bayesian Analysis, 9(3), 557–560.
Shahbaba, B., Yang, Y., & van Dyk, D. A. (2011). Comment on “Data Augmentation for Support Vector Machines.” Bayesian Analysis, 6(1), 31–36.