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<!DOCTYPE html>
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<h1>BayesRx Group</h1>
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<p>We are a group of biostatisticians and data scientists, working at the intersection of statistics, biology and medicine.
Our research explores the potential of Bayesian probabilistic models and machine learning methods to assist in medical and health sciences. These methods are motivated by large and complex datasets such as high-throughput genomics, epigenomics, transcriptomics and proteomics as well as high-resolution neuro- and cancer- imaging. A special focus is on developing integrative models combining different sources of data for biomarker discovery and clinical prediction to aid precision/translational medicine.
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<h2 itemprop="name">Veera Baladandayuthapani</h2>
<h3 itemprop="jobTitle">Professor</h3>
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<a href="http://www.bayesrx.com" target="_blank" itemprop="url" rel="noopener">
<span itemprop="name">University of Michigan</span>
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<h1>Biography</h1>
<p>Dr. Veera Baladandayuthapani is currently a Professor in the Department of Biostatistics at University of Michigan (UM),
where he is also the Director of the Cancer Data Science Shared Resource. He joined UM in Fall 2018 after spending 13 years
in the Department of Biostatistics at University of Texas MD Anderson Cancer Center, Houston, Texas, where was a Professor and Institute Faculty
Scholar and held adjunct appointments at Rice University, Texas A&M University and UT School of Public Health. His research interests are mainly in high-dimensional data modeling and Bayesian inference. This includes functional data analyses, Bayesian graphical models, Bayesian semi-/non-parametric models and Bayesian machine learning. These methods are motivated by large and complex datasets (a.k.a. Big Data) such as high-throughput genomics, epigenomics, transcriptomics and proteomics as well as high-resolution neuro- and cancer- imaging. His work has been published in top statistical/biostatistical/bioinformatics and biomedical/oncology journals. He has also co-authored a book on Bayesian analysis of gene expression data. He currently holds multiple PI-level grants from NIH and NSF to develop innovative and advanced biostatistical and bioinformatics methods for big datasets in oncology. He has also served as the Director of the Biostatistics and Bioinformatics Cores for the Specialized Programs of Research Excellence (SPOREs) in Multiple Myeloma and Lung Cancer and Biostatistics&Bioinformatics platform leader for the Myeloma and Melanoma Moonshot Programs at MD Anderson. He is a fellow of the American Statistical Association and an elected member of the International Statistical Institute. He currently serves as an Associate Editor for Journal of American Statistical Association, Annals of Applied Statstics, Biometrics and Sankhya.</p>
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<h1>Research Snapshot</h1>
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<meta content="2019-05-10 00:00:00 -0400 EDT" itemprop="datePublished">
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<span class="article-date">
<time>May 2019</time>
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<span class="pub-publication">
Overview
</span>
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<a href="/publication/snapshot/">
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<a href="/publication/snapshot/" itemprop="url">BayesRx Group Current & Future Research Efforts</a>
</h3>
<div class="article-style" itemprop="articleBody">
Our current and future research efforts have four main themes shown as four quadrants above. The first is developing Bayesian hierarchical models for multi-platform genomics and imaging data for mechanims and biomarker discovery (top left); Bayesian high-dimensional graphical/netwrok models (top right); development of scalable software for reproducibilty and visualization (bottom left – also see software section) and finally implement these tools in a clinical/translational setting (bottom right). The data sets that feed into these efforts are shown in the corners [see some selected publications below]
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<a href="/tags/bayesian-integrative-models/">
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<h6 style="font-size:90%;">Bayesian Integrative models</h6>
<p style="color:#808080;font-size:80%;">multi-platform genomics and imaging</p>
</a>
</div>
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<a href="/tags/bayesian-graphical/network-models/">
<span class="fas fa-project-diagram fa-6x"> </span>
<h6 style="font-size:90%;">Bayesian graphical/network models</h6>
<p style="color:#808080; font-size:80%;">high-throughput genomic and proteomic</p>
</a>
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<h6 style="font-size:90%;">Functional Data Analyses</h6>
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<h6 style="font-size:90%;">Translational Clinical Applications</h6>
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<h1>Team</h1>
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<h2 class="mb-4">Post-doctoral Fellow</h2>
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<a href="/authors/shariq/"><img class="portrait" src="/author/shariq/avatar_hue1f425098a3388eaf02d5a53685d46e6_1437092_150x150_fill_q90_lanczos_top.jpg" alt="Avatar"></a>
<div class="portrait-title">
<h2><a href="/authors/shariq/">Shariq Mohammed</a></h2>
<p class="people-interests">Bayesian Inference, Variable Selection, Bayesian Machine Learning, Applications in Neuro- and Cancer-Imaging, Precision Health</p>
</div>
</div>
<div class="col-md-12">
<h2 class="mb-4">PhD Students</h2>
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<div class="portrait-title">
<h2><a href="/authors/rupam/">Rupam Bhattacharyya</a></h2>
<p class="people-interests">Bayesian Statistics, Regression Modelling, Statistical Genetics, Precision Oncology, Integrative Omics</p>
</div>
</div>
<div class="col-12 col-sm-auto people-person">
<a href="/authors/yao/"><img class="portrait" src="/author/yao/avatar_hud1b3546e0c2cc472baf84a40376421fb_100761_150x150_fill_q90_lanczos_top.jpg" alt="Avatar"></a>
<div class="portrait-title">
<h2><a href="/authors/yao/">Tsung-Hung Yao</a></h2>
<p class="people-interests">Diffusion Tree Modeling, Bayesian Nonparametric Statistics, Precision Medicine</p>
</div>
</div>
</div>
</div>
</section>
<section id="alumni" class="home-section wg-blank " >
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<h1>Alumni</h1>
<ul>
<li><p>Postdoctoral Researchers</p>
<ul>
<li><a href="https://sites.google.com/site/rosselldavid/" target="_blank">David Rossell Ribera</a>, 2006-2007, (with Valen E. Johnson)