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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/codes.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: 1393px;}.shape_6 {background-color:#F0F0F0;opacity: 1.00; filter:alpha(opacity=100);}.shape_9 {background-color:#677284;opacity: 1.00; filter:alpha(opacity=100);}</style></head><body><div class="container" style="height:1393px"><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="9E22D4B80F1046C0AF65222FDD035EB9">HOME</a></li><li><a class="main item1" href="research.html" data-linkuuid="9E22D4B80F1046C0AF65222FDD035EB9">RESEARCH</a></li><li><a class="main item2" href="publication.html" data-linkuuid="9E22D4B80F1046C0AF65222FDD035EB9">PUBLICATION</a></li><li><a class="main item3" href="teaching.html" data-linkuuid="9E22D4B80F1046C0AF65222FDD035EB9">TEACHING</a></li><li><a class="main item4" href="activities.html" data-linkuuid="9E22D4B80F1046C0AF65222FDD035EB9">ACTIVITIES</a></li><li style='margin-right:0px;' class="last"><a class="main item5" href="codes.html" data-linkuuid="9E22D4B80F1046C0AF65222FDD035EB9">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:343px;width:100%;min-width:800px;height:908px;z-index:6;position: absolute;"></div><div style="position:relative"><div class="shape_7" style="left:0px;top:342.5px;width:800px;height:917px;z-index:7;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">CODES</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">These are the codes we have developed for our research projects. Please contact me if you need help with using them. You can also download our codes from <a href="https://github.com/babaks" class="linkStyle_12">GitHub</a><span class="Style8">.</span></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">Grassmann HMC</p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">We have developed a new dimensionality reduction method using Grassmann HMC. This is a joint work with Andrew Holbrook and Alexander Vandenberg.</p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><a href="ewExternalFiles/GHMC_Code.zip" class="linkStyle_25">Codes</a></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">A Dynamic Bayesian Model for Neuronal Interactions</p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">We have developed a dynamic Bayesian method (published in JASA) for analyzing multiple neurons. This is a joint work with Bo Zhou, Sam Behseta, Hernando Ombao, and David Moorman.</p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><a href="ewExternalFiles/DynamicCodes.zip" class="linkStyle_25">Codes</a></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">Detecting Synchrony Among Multiple Neurons </p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">We have developed a new approach for detecting multiway synchrony among neurons. For this project, I worked with my students, Bo Zhou and Shiwei Lan, and my collaborators, Sam Behseta, Hernando Ombao, and David Moorman. </p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><a href="ewExternalFiles/MultiWaySynchrony.zip" class="linkStyle_25">Codes</a></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">Wormhole Hamiltonian Monte Carlo (WHMC)</p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">My student, Shiwei Lan, and I have worked on this project with Professor Jeff Streets. Our method can sample from multimodal distributions even in high dimensions. You can obtain the codes and learn more about this method from <a href="http://users.cms.caltech.edu/~slan/slan/Home.html" class="linkStyle_25">Shiwei Lan</a><span class="Style8">'s website.</span></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">Lagrangian Monte Carlo (LMC)</p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Shiwei Lan, Vassilios Stathopoulos, Mark Girolami and I have have developed a new algorithm called Lagrangian Dynamical Monte Carlo. This is a fully explicit integrator for Riemannian Manifold Monte Carlo (Girolami and Calderhead, 2011). We show that our method is in fact equivalent to transforming Riemannian Hamiltonian dynamics to Lagrangian dynamics. HYou can obtain the codes and learn more about this method from <a href="http://users.cms.caltech.edu/~slan/slan/Home.html" class="linkStyle_25">Shiwei Lan</a><span class="Style8">'s website.</span></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">Split Hamiltonian Monte Carlo (Split HMC)</p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">With Radford Neal and Shiwei Lan, we have proposed a new approach to improve computational efficiency of Hamiltonian Monte Carlo (HMC). Our approach is based on "splitting" the Hamiltonian such that much of the movement around the state space is performed at low computational cost. </p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><a href="ewExternalFiles/SplitHMC.zip" class="linkStyle_25">Codes</a></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">Nonlinear models using Dirichlet process mixtures </p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">We introduced a new nonlinear method based on modeling the joint distribution of response and covariates using the Dirichlet process mixtures of linear models. This was a joint work with Radford Neal. </p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><a href="ewExternalFiles/DPM.zip" class="linkStyle_25">Codes</a></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">Hierarchical Classification </p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">These are some files related to our work (with Radford Neal) on classification models when classes have a hierarchical structure.</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"><a href="ewExternalFiles/MNL.m" class="linkStyle_25">MNL</a><span class="Style8">: This is a simple Bayesian multinomial model (i.e., taking classes as unrelated entities)</span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><a href="ewExternalFiles/treeMNL.m" class="linkStyle_25">treeMNL</a><span class="Style8">: This models hierarchical classes using nested multinomial logit models. </span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><a href="ewExternalFiles/corMNL.m" class="linkStyle_25">corMNL</a><span class="Style8">: This is our proposed method that takes the hierarchy as a prior.</span></p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><a href="ewExternalFiles/makeTree.m" class="linkStyle_25">makeTree</a><span class="Style8">: This code gets a matrix of classes and returns a tree structure.</span></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">Bayesian Relevance Determination (BRD)</p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6">Wes Johnson and I have developed a new nonparametric Bayesian method to divide genes into several subgroups according to their degree of relevance to the outcome of interest (i.e., disease status). </p><p style="line-height:16px;margin-bottom:0px;margin-top:0px;" class="Style6"><a href="ewExternalFiles/BRD_Codes.zip" class="linkStyle_25">Codes</a></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></div></div><div style="position:relative"><div class="shape_8" style="left:275.5px;top:373px;width:243px;height:20px;z-index:8;position: absolute;"><img src="images/codes/shape_8.png" height="20" width="243" alt="(placeholder)" data-src2x="images/codes/shape_8@2x.png" srcset="images/codes/shape_8.png 1x, images/codes/shape_8@2x.png 2x" /></div></div><div class="shape_9" style="left:0;top:1251px;width:100%;min-width:800px;height:138px;z-index:9;position: absolute;"></div><div style="position:relative"><div style="left:486px;top:1251px;height:138px;width:314px;position: absolute;z-index: 10;" 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_11" style="left:258.5625px;top:1353.5px;width:191px;height:39px;z-index:11;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_12" style="left:259.0156px;top:1307px;width:204px;height:48px;z-index:12;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_13" style="left:220.5px;top:1264.5px;width:243px;height:43px;z-index:13;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 style="position:relative"><div class="shape_14" style="left:221px;top:1350px;width:27px;height:27px;z-index:14;position: absolute;"><img src="images/codes/telephone.png" height="27" width="27" data-src2x="images/codes/telephone@2x.png" srcset="images/codes/telephone.png 1x, images/codes/telephone@2x.png 2x" /></div></div><div style="position:relative"><a href="mailto:babaks@uci.edu?subject=" class="outerlink"><div class="shape_15" style="left:221px;top:1308px;width:31px;height:25px;z-index:15;position: absolute;"><img src="images/codes/mail.png" height="25" width="31" data-src2x="images/codes/mail@2x.png" srcset="images/codes/mail.png 1x, images/codes/mail@2x.png 2x" /></div></a></div><div style="position:relative"><div class="shape_16" style="left:0px;top:1251px;width:208px;height:138px;z-index:16;position: absolute;"><img src="images/codes/FatherSon.jpg" height="138" width="208" data-src2x="images/codes/FatherSon@2x.jpg" srcset="images/codes/FatherSon.jpg 1x, images/codes/FatherSon@2x.jpg 2x" /></div></div></div><footer data-top='1393' data-height='0'></footer></div></body></html>