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<!DOCTYPE html><html><head><title></title><link rel="shortcut icon" href="/favicon.ico" /><meta http-equiv="Content-type" content="text/html;charset=UTF-8" /><meta name="viewport" content="width=800" /><meta name="description" content="" /><meta name="generator" content="EverWeb 2.9.1 (2184)" /><meta name="buildDate" content="Tuesday, December 26, 2023" /><meta property="og:url" content="https://www.ics.uci.edu/~babaks/publication.html" /><meta property="og:type" content="website" /> <link rel="stylesheet" type="text/css" href="ew_css/textstyles.css?3786495355" /><link rel="stylesheet" type="text/css" href="ew_css/responsive.css?3786495355" /><script type="text/javascript" src="ew_js/imageCode.js"></script><link rel="stylesheet" type="text/css" href="ew_css/italy/pagestyle.css?3.786467e+9" /><style type="text/css">div.container {min-height: 3549px;}.shape_6 {background-color:#677284;opacity: 1.00; filter:alpha(opacity=100);}.shape_11 {background-color:#F0F0F0;opacity: 1.00; filter:alpha(opacity=100);}</style></head><body><div class="container" style="height:3549px"><header><div style="position:relative"><div class="ewnavmenu" id="navmenu_menu0" style="left:123px;top:21px;height:26px;width:556px;z-index:10000;position: absolute;"><ul id="navigation_menu0"><li><a class="main item0" href="index.html" data-linkuuid="37E8443D61D74C1B8627E38ADE67782D">HOME</a></li><li><a class="main item1" href="research.html" data-linkuuid="37E8443D61D74C1B8627E38ADE67782D">RESEARCH</a></li><li><a class="main item2" href="publication.html" data-linkuuid="37E8443D61D74C1B8627E38ADE67782D">PUBLICATION</a></li><li><a class="main item3" href="teaching.html" data-linkuuid="37E8443D61D74C1B8627E38ADE67782D">TEACHING</a></li><li><a class="main item4" href="activities.html" data-linkuuid="37E8443D61D74C1B8627E38ADE67782D">ACTIVITIES</a></li><li style='margin-right:0px;' class="last"><a class="main item5" href="codes.html" data-linkuuid="37E8443D61D74C1B8627E38ADE67782D">CODES</a></li></ul></div></div><div class="shape_1" style="left:0;top:0.125px;width:100%;min-width:800px;height:50px;z-index:1;position: absolute;"></div><div style="position:relative"><div class="shape_2" style="left:-0.5px;top:12.625px;width:139px;height:37px;z-index:2;position: absolute;"><div style="padding: 4.75px 2.16px 4.32px 2.16px; "><p style="line-height:17px;margin-top:0px;margin-bottom:11px;" class="Style43">Babak Shahbaba</p></div></div></div><div style="position:relative"><div class="shape_3" style="left:0.75px;top:51px;width:797px;height:292px;z-index:3;position: absolute;"><img src="masterfiles/italy/images/Italy.jpg" height="292" width="797" data-src2x="masterfiles/italy/images/Italy@2x.jpg" srcset="masterfiles/italy/images/Italy.jpg 1x, masterfiles/italy/images/Italy@2x.jpg 2x" /></div></div><div style="position:relative"><div class="shape_4" style="left:470.5px;top:101px;width:267px;height:20px;z-index:4;position: absolute;"><img src="masterfiles/italy/images/shape_4.png" height="20" width="267" alt="(placeholder)" data-src2x="masterfiles/italy/images/shape_4@2x.png" srcset="masterfiles/italy/images/shape_4.png 1x, masterfiles/italy/images/shape_4@2x.png 2x" /></div></div><div style="position:relative"><div class="shape_5" style="left:417px;top:53px;width:382px;height:156px;z-index:5;position: absolute;"><p style="line-height:14px;text-align:center;margin-top:0px;margin-bottom:0px;" class="Style44">Babak Shahbaba, PhD</p><p style="line-height:14px;text-align:center;margin-bottom:0px;margin-top:0px;" class="Style44">Professor of Statistics and Computer Science </p><p style="line-height:14px;text-align:center;margin-bottom:0px;margin-top:0px;" class="Style44">Director of The UCI Data Science Initiative</p><p style="line-height:14px;text-align:center;margin-bottom:0px;margin-top:0px;" class="Style44">University of California, Irvine</p><p style="line-height:9.8px;text-align:center;margin-bottom:0px;margin-top:0px;" class="Style45"><br /></p><p style="line-height:14px;text-align:center;margin-bottom:0px;margin-top:0px;" class="Style44">Scalable Bayesian Inferences</p><p style="line-height:14px;text-align:center;margin-bottom:0px;margin-top:0px;" class="Style44">Nonparametric Bayesian Methods</p><p style="line-height:14px;text-align:center;margin-bottom:0px;margin-top:0px;" class="Style44">Statistical Methods in Biological Sciences </p></div></div></header><div class="content" data-minheight="0"><div class="shape_6" style="left:0;top:3407px;width:100%;min-width:800px;height:138px;z-index:6;position: absolute;"></div><div style="position:relative"><div style="left:486px;top:3407px;height:138px;width:314px;position: absolute;z-index: 7;" class=""><!--314--><iframe width="100%" height="138" frameborder="0" scrolling="no" marginheight="0" marginwidth="0" src="https://www.google.com/maps?f=q&source=s_q&hl=en&geocode=&q=6210 Donald Bren Hall, Irvine, CA 92697&ie=UTF8&output=embed"></iframe></div></div><div style="position:relative"><div class="shape_8" style="left:258.5625px;top:3509.5px;width:191px;height:39px;z-index:8;position: absolute;"><div style="padding: 4.32px 4.32px 4.32px 4.32px; "><p style="line-height:18.75px;margin-top:0px;margin-bottom:11px;" class="Style53">(949) 824-0623</p></div></div></div><div style="position:relative"><div class="shape_9" style="left:259.0156px;top:3463px;width:204px;height:48px;z-index:9;position: absolute;"><div style="padding: 4.32px 4.32px 4.32px 4.32px; "><p style="line-height:9px;margin-top:0px;margin-bottom:11px;" class="Style53">2222 ISEB, UC Irvine, CA 92697</p><p style="line-height:9px;margin-bottom:11px;margin-top:0px;" class="Style53">babaks at uci dot edu</p></div></div></div><div style="position:relative"><div class="shape_10" style="left:220.5px;top:3420.5px;width:243px;height:43px;z-index:10;position: absolute;"><div style="padding: 4.32px 2.16px 4.32px 2.16px; "><p style="line-height:23px;margin-top:0px;margin-bottom:11px;" class="Style54">Contact</p></div></div></div><div class="shape_11" style="left:0;top:343px;width:100%;min-width:800px;height:3064px;z-index:11;position: absolute;"></div><div style="position:relative"><div class="shape_12" style="left:221px;top:3506px;width:27px;height:27px;z-index:12;position: absolute;"><img src="images/publication/telephone.png" height="27" width="27" data-src2x="images/publication/telephone@2x.png" srcset="images/publication/telephone.png 1x, images/publication/telephone@2x.png 2x" /></div></div><div style="position:relative"><div class="shape_13" style="left:0px;top:343px;width:800px;height:3068px;z-index:13;position: absolute;"><p style="line-height:12px;text-align:center;margin-top:0px;margin-bottom:0px;" class="Style4"><br /></p><p style="line-height:34.5px;text-align:center;margin-bottom:0px;margin-top:0px;" class="Style5">PUBLICATIONS</p><p style="line-height:12px;text-align:center;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:19px;margin-bottom:0px;margin-top:0px;" class="Style2">Books</p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">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. </p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">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. </p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Shahbaba, B. (2012), Biostatistics with R, An Introduction to Statistics Through Biological Data, Springer.</p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:19px;margin-bottom:0px;margin-top:0px;" class="Style2">Papers</p><p style="line-height:19px;margin-bottom:0px;margin-top:0px;" class="Style2"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Ren, Y., Shahbaba, B., and Stark, C. (2023), Improving clinical efficiency in screening for cognitive impairment due to Alzheimer’s, <span class="Style7">Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring </span><span class="Style8">(to appear)</span><span class="Style7">.</span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Tran, B., Shahbaba, B., Mandt, S., Filippone, M. (2023), Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes, <span class="Style7">ICML</span><span class="Style8"> 2023 (to appear).</span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Denti, F., Azevedo, R., Lo, C., Wheeler, D. G. , Gandhi, S. P. , Guindani, M., and Shahbaba, B. “A horseshoe</p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">mixture model for Bayesian screening with an application to light sheet fluorescence microscopy in</p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">brain imaging,” The Annals of Applied Statistics, vol. 17, no. 3, pp. 2639 – 2658, 2023.</p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"> </p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">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, N<span class="Style7">ature Communications,</span><a href="https://www.nature.com/articles/s41467-022-28057-6" class="linkStyle_12"> online</a><span class="Style8">. </span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Lan, S., Li, S., and Shahbaba, B. (2022), Scaling Up Bayesian Uncertainty Quantification for Inverse Problems Using Deep Neural Networks, S<span class="Style7">IAM/ASA Journal on Uncertainty Quantification</span><span class="Style8">, 10:4, 1684-1713.</span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">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 ,<span class="Style7"> JAMA Network Open</span><span class="Style8"> (to appear).</span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Martinez Lomeli, L., Iniguez, A., Shahbaba, B., Lowengrub, J. S., and Minin, V. (2021), Optimal Experimental Design for Mathematical Models of Hematopoiesis, <span class="Style7">Journal of Royal Society Interface</span><span class="Style8"> (to appear).</span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">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). </p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">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, <span class="Style7">Neurorehabilitation and Neural Repair</span><span class="Style8"> (to appear). </span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">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. </p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Shahbaba, B., Lan, S., Streets, J. D., and Holbrook, A. J. (2020), Nonparametric Fisher Geometry with Application to Density Estimation, <span class="Style7">Uncertainty in Artificial Intelligence (UAI 2020)</span><span class="Style8">. </span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Erani et al. (2020+), EEG Improves Diagnosis of Acute Stroke and Large Vessel Occlusion, <span class="Style7">Stroke</span><span class="Style8"> (to appear).</span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">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, <span class="Style7">Neurophotonics</span><span class="Style8">, 6(4), 045012.</span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Shahbaba, B., Lomeli, L. M., Chen, T, Lan, S. (2019), Deep Markov Chain Monte Carlo, https://arxiv.org/abs/1910.05692.</p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">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, <span class="Style7">Bayesian Analysis</span><span class="Style8"> (to appear).</span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Li, L., Pluta, D., Shahbaba, B., Fortin, N., Ombao, H., Baldi, P. (2019), Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes, <span class="Style7">NeurIPS 2019, Vancouver</span><span class="Style8">.</span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Li, L., Holbrook, A., Shahbaba, B, Baldi, P. (2019), Neural Network Gradient Hamiltonian Monte Carlo, <span class="Style7">Computational Statistics, </span><span class="Style8">34(1), 281-299.</span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Baldi, P. and Shahbaba, B. (2019+), Bayesian Causality, <span class="Style7">The American Statistician</span><span class="Style8"> (to appear).</span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">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, <span class="Style7">Statistica Sinica</span><span class="Style8"> (to appear).</span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Zhang, C., Shahbaba, B., Zhao, H. (2018), Variational Hamiltonian Monte Carlo via Score Matching, <span class="Style7">Bayesian Analysis</span><span class="Style8">, 13(2), 485-506.</span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">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, <span class="Style7">Journal of Statistical Computation and Simulation, </span><span class="Style8">88(5), 982-1002.</span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">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, <span class="Style7">Stat, </span><span class="Style8">6 (1), 53-67.</span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Zhang, C., Shahbaba, B., Zhao, H. (2017), Hamiltonian Monte Carlo Acceleration Using Surrogate Functions with Random Bases, <span class="Style7">Statistics and Computing</span><span class="Style8">, 27, 1473-1490.</span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Gao, X., Shahbaba, B., and Ombao, H. (2017) Modeling Binary Time Series Using Gaussian Processes With Application to Predicting Sleep States, <span class="Style7">Journal of Classification</span><span class="Style8"> (to appear).</span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Shahbaba, B. (2017), Review of ``Handbook of Discrete-Valued Time Series,'' edited by Davis, R.A., Holan, S.H., Lund, R., Ravishanker, N., <span class="Style7">Journal of the American Statistical Association</span><span class="Style8">, 112(520), 1771.</span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Shahbaba, B. (2017), Review of ``Fundamentals of Statistical Experimental Design and Analysis,'' by Robert G. Easterling, <span class="Style7">The American Statistician</span><span class="Style8">, 71(4), 369. </span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">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, <span class="Style7">The Prostate</span><span class="Style8">, 78(4):294-299. </span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">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, <span class="Style7">Journal of Cancer,</span><span class="Style8"> 8(13), 2554-2560.</span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Zhang, C., Shahbaba, B., Zhao, H. (2017), Precomputing strategy for Hamiltonian Monte Carlo Method based on regularity in parameter space, <span class="Style7">Computational Statistics</span><span class="Style8">, 32(1), 253-279.</span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">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.</p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">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, <span class="Style7">Journal of the American Statistical Association</span><span class="Style8">, 111 (514), 459-471.</span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Agostinelli, F., Ceglia, N., Shahbaba, B., Sassone-Corsi, P., Baldi, P., What Time is it? Deep Learning Approaches for Circadian Rhythms (2016), <span class="Style7">Bioinformatics</span><span class="Style8">, 32(12), i8-i17.</span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Shahbaba, B. (2016), Review of "Geometry Driven Statistics," edited by Dryden I.L. ad Kent, J.T., <span class="Style7">Journal of the American Statistical Association, </span><span class="Style8">111(516), 1840.</span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Lan, S., Palacios, J., Karcher, M., Minin, V., Shahbaba, B. (2015) An Efficient Bayesian Inference Framework for Coalescent-Based Nonparametric Phylodynamics, <span class="Style7">Bioinformatics</span><span class="Style8">, 31(20), 3282-3289.</span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">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, <span class="Style7">Biological Psychiatry, </span><span class="Style8">79(10):831-9.</span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Lan, S., Stathopoulos, V., Shahbaba, B., and Girolami, M. (2015), Markov Chain Monte Carlo from Lagrangian Dynamics (2015), <span class="Style7">Journal of Computational and Graphical Statistics</span><span class="Style8">, 24(2), 357-378.</span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Vandenberg-Rodes, A. and Shahbaba, B. (2015), Dependent Matérn Processes for Multivariate Time Series, , arXiv:1502.03466.</p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">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, <span class="Style7">Annals of Neurology</span><span class="Style8">, 77(1), 132-45.</span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Shahbaba, B. (2015), Review of "Analysis of Neural Data,'' by Kass, R.E., Eden, U., and Brown, E., <span class="Style7">Journal of the American Statistical Association</span><span class="Style8">, 110(510), 578.</span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Shahbaba, B. (2015), Review of "Applied Statistical Inference: Likelihood and Bayes,'' by Held, L. and Sabanés Bové, D., <span class="Style7">Journal of the American Statistical Association</span><span class="Style8">, 110(510), 579.</span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Shahbaba, B., Comment on “Robust Bayesian Graphical Modeling Using Dirichlet t-distribution,” <span class="Style7">Bayesian Analysis,</span><span class="Style8"> 9(3), 557-560.</span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Lan, S., Zhou, B., and Shahbaba, B. Spherical Hamiltonian Monte Carlo for Constrained Target Distributions, <span class="Style7">ICML</span><span class="Style8"> 2014. </span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Ahn, S., Shahbaba, B., and Welling, M., Distributed Stochastic Gradient MCMC, <span class="Style7">ICML</span><span class="Style8"> 2014.</span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Shahbaba, B., Lan, S., Streets, J., Comment on “Geodesic Monte Carlo on Embedded Manifolds,” <span class="Style7">Scandinavian Journal of Statistics</span><span class="Style8">, 41(1), 14-15.</span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Lan, S., Streets, J., and Shahbaba, B. Wormhole Hamiltonian Monte Carlo, <span class="Style7">AAAI</span><span class="Style8"> 2014.</span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Shahbaba, B., Zhou, B., Lan, S., Ombao, H., Moorman, D., and Behseta, S., A Semiparametric Bayesian Model for Detecting Synchrony Among Multiple Neurons, <span class="Style7">Neural Computation</span><span class="Style8">, 26(9), 2025-51.</span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Shahbaba, B., Lan, S., Johnson, W.O. , Neal, R.M., Split Hamiltonian Monte Carlo, <span class="Style7">Statistics and Computing</span><span class="Style8">, 24(3), 339-349. </span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">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, <span class="Style7">PLOS ONE</span><span class="Style8">, 8(4):e61197. doi: 10.1371. </span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Shahbaba, B., Johnson, W.O. (2013), Bayesian Nonparametric Variable Selection as an Exploratory Tool for Discovering Differentially Expressed Genes, <span class="Style7">Statistics in Medicine</span><span class="Style8">, 30(12), 2114-26. </span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">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, <span class="Style7">PNAS</span><span class="Style8">, 109(20):E1312–9. </span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">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, <span class="Style7">Psychometika</span><span class="Style8">, 78(12), 260-278. </span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Shahbaba, B, Shachaf, CM, and Yu, Z (2012), A pathway analysis method for genome-wide association studies, <span class="Style7">Statistics in Medicine</span><span class="Style8">, 31(10), 988-1000. </span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Shahbaba, B, Yu, Y, and van Dyk, DA (2011), Comment on "Data Augmentation for Support Vector Machines'', <span class="Style7">Bayesian Analysis</span><span class="Style8">, 6(1), 31-36.</span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Shahbaba B, Tibshirani R, Shachaf, CM, and Plevritis SK (2011), Bayesian gene set analysis for identifying significant biological </p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">pathways, <span class="Style7">Journal of the Royal Statistical Society, Series C</span><span class="Style8">, Volume 60, Issue 4, 541-557. </span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Shahbaba B, Neal RM (2009), Nonlinear models using Dirichlet process mixtures, <span class="Style7">Journal of Machine Learning Research</span><span class="Style8">, Volume 10, 1829-1850. </span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Shahbaba B, Gentles AJ, Beyene J, Plevritis SK, Greenwood CMT (2009), A Bayesian nonparametric method for model evaluation: Application to genetic studies,<span class="Style7"> Journal of Nonparametric Statistics</span><span class="Style8">, Volume 21, Issue 3, 379 - 396. </span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">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, <span class="Style7">Blood</span><span class="Style8">, 114(15), 3158 - 3166. </span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Shahbaba, B (2009), Discovering hidden structures using mixture models: Application to nonlinear time series processes, <span class="Style7">Studies in Nonlinear Dynamics & Econometrics</span><span class="Style8">, Vol. 13, No. 2, Article 5.</span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Shahbaba B, Neal RM (2007) Improving classification when a class hierarchy is available using a hierarchy-based prior, <span class="Style7">Bayesian Analysis</span><span class="Style8">, 2(1), 221-238. </span></p><p style="line-height:12px;margin-bottom:0px;margin-top:0px;" class="Style4"><br /></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Shahbaba B, Neal RM (2006), Gene function classification using Bayesian models with hierarchy-based priors, <span class="Style7">BMC Bioinformatics</span><span class="Style8">, 7:447.</span></p></div></div><div style="position:relative"><a href="mailto:babaks@uci.edu?subject=" class="outerlink"><div class="shape_14" style="left:221px;top:3464px;width:31px;height:25px;z-index:14;position: absolute;"><img src="images/publication/mail.png" height="25" width="31" data-src2x="images/publication/mail@2x.png" srcset="images/publication/mail.png 1x, images/publication/mail@2x.png 2x" /></div></a></div><div style="position:relative"><div class="shape_15" style="left:0px;top:3407px;width:208px;height:138px;z-index:15;position: absolute;"><img src="images/publication/FatherSon.jpg" height="138" width="208" data-src2x="images/publication/FatherSon@2x.jpg" srcset="images/publication/FatherSon.jpg 1x, images/publication/FatherSon@2x.jpg 2x" /></div></div><div style="position:relative"><div class="shape_16" style="left:275.5px;top:373px;width:243px;height:20px;z-index:16;position: absolute;"><img src="images/publication/shape_16.png" height="20" width="243" alt="(placeholder)" data-src2x="images/publication/shape_16@2x.png" srcset="images/publication/shape_16.png 1x, images/publication/shape_16@2x.png 2x" /></div></div></div><footer data-top='3549' data-height='0'></footer></div></body></html>