Faculty

Chan, Cliburn
Cliburn Chan, Center Director of Quantitative Immunology, Associate Professor

Together with Georgia Tomaras, Cliburn founded the center to create a community for human systems immunology research at Duke. Originally trained as a medical doctor in the National University of Singapore, Cliburn subsequently completed a PhD developing mathematical models of the immune response at University College London, followed by postdoctoral fellowships in a molecular immunology laboratory at Imperial college London and modeling host-pathogen interactions the Statistical and Applied Mathematical Sciences Institute (SAMSI) in North Carolina before joining the faculty in Biostatistics and Bioinformatics at Duke. His current research interests are the modeling of host-viral interactions (HIV, CMV and EBV) and statistical inference from single cell data. Cliburn also leads the Quantitative Sciences Core for the Duke Center for AIDS Research (CFAR) and the Quantitative Methods in HIV/AIDS research training program.

Email: cliburn.chan@duke.edu

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Georgia Tomaras, Center Director of Infection and Vaccination, Professor

Dr. Tomaras’ overall research program is to understand the cellular and humoral immune response to HIV-1 infection and vaccination that are involved in protection from HIV-1. The research in the Tomaras laboratory centers around three main projects involving 1) antiviral CD8 T cell responses in HIV-1 infection and post vaccination, 2) mucosal and systemic antibody responses to infection and vaccination in both non-human primates and humans and 3) the ontogeny of neutralizing antibodies in HIV-1 infection. Her laboratory is also within the Duke Human Vaccine Institute.

Email: georgia.tomaras@duke.edu

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Jessilyn Dunn, Assistant Professor of Biomedical Engineering

Dr. Jessilyn Dunn is an Assistant Professor of Biomedical Engineering. Her primary areas of research focus on biomedical data science and mobile health; her work includes multi-omics, wearable sensor, and electronic health records integration and digital biomarker discovery.

Email: jessilyn.dunn@duke.edu

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Josh Granek, Bioinformatics and Reproducible Analysis group Leader, Assistant Professor

Josh Granek is a biologist and bioinformatician with broad interests in using genomics to understand how the immune system interacts with microbes. This interest includes the roles played by both beneficial and harmful bacteria, fungi, and viruses.  We study single microbes and microbiomes, primarily using high-throughput sequencing methods.  We have a particular interest in developing new experimental and analytical methods that leverage the power of high-throughput sequencing.  We are also interested in using deep learning in microbiology research.

Email:joshua.granek@duke.edu

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Yongtao Guan, Immuno Genomics Faculty Leader, Assistant Professor

Dr. Guan is a statistical geneticist specialized in developing statistical models and computational methods to analyze genetic and genomic data. He studied probability and stochastic processes, as well as classical population genetics theory, with Professors Steve Krone and Paul Joyce at the University of Idaho. The highlight of his Ph.D. work is the invention of the small-world Markov chain Monte Carlo algorithm and the theoretical study of its polynomial convergence properties. Dr. Guan became a Bayesian statistician after extended postdoctoral training with Professor Matthew Stephens at the University of Chicago. During which he worked on Bayesian imputation-based association mapping, and Bayesian variable selection regression for multi-SNP association mapping. In 2010, Dr. Guan moved to Baylor College of Medicine to start his own lab. During the eight years there his lab has been supported by research grants from National Institute of Health, by research contracts from the United States Department of Agriculture, and by collaborative agreements with Beijing Scisoon Medical Genetic Laboratory. He joined Duke faculty in the summer of 2018.

Email: yongtao.guan@duke.edu

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Zhicheng Ji, Assistant Professor

Zhicheng Ji received his Ph.D. in Biostatistics and MSE in Computer Science from Johns Hopkins University. He is a computational biologist and biostatistician who develops novel statistical and computational methods for analyzing high-throughput sequencing data, especially data from single-cell sequencing. He applies these methods to study gene expression and gene regulatory programs in various biological systems. He is now interested in developing new methods integrating large-scale genomic data to understand the immune response to different antigens.

Email: zhicheng.ji@duke.edu

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Jichun Xie, Statistical Inference Faculty Leader, Associate Professor

Jichun Xie is Associate Professor of Biostatistics and Bioinformatics at Duke University. Her research is focused on developing computational and statistical methods for extracting meaningful information from omics data. Her research sits at the intersection of statistical inference, machine learning, bioinformatics, and immunology.

Email: jichun.xie@duke.edu

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Jason Xu, Assistant Professor of Statistical Science

I develop methods for learning from complex, often high-dimensional data arising in a broad range of scientific applications. One theme of my research is to develop theory and methods for fitting stochastic models to partially observed data. These models may describe the structure and rates of blood cell differentiation, or the spread of an epidemic over a heterogeneous population over a dynamic contact structure, but typically are informed by data that only provide a snapshot of the complete process. We apply ideas from both sampling and optimization perspectives to enable rigorous inferential tools such as likelihood-based methods. I am also interested in iterative optimization algorithms for tasks such as clustering, constrained estimation, and regression under non-convex penalties. Here we develop interpretable and scalable algorithms with a focus on simplicity, and often fashion such optimization routines from the perspective of majorization-minimization.

Research interests: stochastic modeling, machine learning, computational statistics, optimization, missing data

Email: jason.q.xu@duke.edu

Staff

Richard Barfield
Richard Barfield, Biostatistician III

Richard received his PhD from Harvard University in 2017 and his MPH from Emory University in 2012. He is currently working with researchers within the Immunology Center. His past research was working with DNA methylation microarray data and methods development for summary Mendelian Randomization studies. His primary research interests are mediation analysis, Mendelian Randomization, large-scale omics analyses, and statistical immunology.

Email: richard.barfield@duke.edu

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David Beaumont, Data Administration Analyst

David is an application and data management specialist who has deployed information systems and process pipelines to support programs for various clinical networks, research centers, and academic institutions. With expertise in data governance and provenance, he has specialized in meeting regulatory compliance for systems utilized in clinical research environments. For the past 5 years David has focused on providing tools and operational support needed to facilitate data streams through the study life cycle at DHVI. He currently leads The Data & Application Management Team under Dr. Tomaras at CHSI, and is Director of the Clinical Compliance Core, a multi-disciplinary systems validation group supporting laboratories under the Department of Surgery – Division of Surgical Sciences.

Email: david.beaumont@duke.edu

Sheetal Sawant, Biostatistician II

Sheetal received her MPH in Biostatistics, from University of Nebraska Medical Centre (2012), and is a Certified Base Programmer for SAS 9.  She has over six years of experience working for diverse public health and research environments, mainly focusing on clinical research, design of medical studies and analysis. Sheetal’ s main areas of research include Biostatistical analysis in Virology, and Immunology. Her current work profile covers data processing, quality control, analysis and interpretation of high throughput data (multiplex binding antibody, Fc Function, antibody dependent cellular phagocytosis). She collaborates with scientists working on immune responses to HIV infection and vaccines, from four major domains: the Collaboration for AIDS Vaccine Discovery [CAVD], the HIV Vaccine Trials Network [HVTN], non-human primate studies [NHP], and the Military Health Research Program [MHRP].

Email: sheetal.sawant@duke.edu

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Rachel Spreng, Biostatistician III

Rachel received a PhD from North Carolina State University in 2016. She has computational experience in a variety of fields, including physics, astronomy, genetics, and immunology. She works in support of researchers in the center and the Duke Human Vaccine Institute to study diseases such as malaria, influenza, and HIV-1. Her current research interests are method development for cross-platform analyses and identification of immune correlates of protection.

Email: rachel.spreng@duke.edu

Scott White, Scientific Programmer

Scott has over 10 years of experience in software development and data management for medical research and data science, with expertise in image analysis and flow cytometry. He has developed image analysis pipelines for multiple modalities including MRI, X-ray, and immuno-fluorescence (IF) microscopy. For the past 7 years, he has worked as a Scientific Programmer in the Department of Biostatistics and Bioinformatics, developing Python libraries for automated analysis of flow cytometry data and IF microscopy, as well as assisting graduate students and faculty with various software development projects.

Email: scott.white@duke.edu

Lu Zhang, Biostatistician II

Lu Zhang earned her Master in Statistics from UNC-Chapel Hill, and has a few years of pharmaceutical research experience and over three years of experience on clinical research data analysis. Currently, her major focuses are processing human and pre-clinical data and providing multiplex binding antibody assay data management, data analysis, and other statistics related support to HIV vaccine research work. This work is mainly from two domains: HIV Vaccine Trials Network (HVTN) and non-Human Primate Studies (NHP).

Email:  lu.zhang809@duke.edu

Fellows, Post-Docs, and Students

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Stephanie Langel, Postdoctoral Fellow, I4Q Coordinator

Stephanie Langel, PhD is a postdoctoral fellow in the lab of Dr. Sallie Permar studying maternal and neonatal immunity, breast milk correlates of immune protection, and mucosal vaccination strategies for prevention of enteric and respiratory diseases. She also contributes to a monthly podcast ‘Immune’ about the body’s immune system (www.microbe.tv/immune).

 

Email: stephanie.langel@duke.edu

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César J. López Angel, Duke Pediatric Research Scholar

César trained in medicine and immunology in the MD/PhD program at Stanford University, where he utilized high-throughput immune monitoring modalities, and a systems immunology analytical pipeline to characterize the effects of chronic infection and aging on immune homeostasis, vaccination, and immune signaling networks. César’s current work in collaboration with the HIV Vaccine Trials Network and under the mentorship of Dr. Georgia Tomaras, focuses on determining whether baseline or post-HIV vaccine immune composition, cellular function, or systemic cytokines affect risk of acquiring HIV, and whether preexisting CMV immunity modifies the relative risk of HIV acquisition. César is currently a resident in pediatrics in the Duke Pediatric Research Scholars Program, and is interested in sub-specializing in infectious diseases with a focus on vaccine development.

Email: cesar.lopez.angel@duke.edu

Elliot Sorelle
Elliot Sorelle, Postdoctoral Scholar

Elliott received his PhD in Biophysics from Stanford University in 2018. His current project under the direction of Dr. Micah Luftig and Dr. Cliburn Chan focuses on collecting and utilizing combined time-resolved microscopy and gene expression data from individual B lymphocytes in a highly parallel fashion to predictively model cell fates following infection with Epstein-Barr Virus (EBV). Following EBV infection, B cells can undergo a variety of fates ranging from growth arrest and cell death to transformation into immortalized lymphoblastoid cell lines, the latter of which can lead to lymphoproliferative disease in the context of immunosuppression. The project, which comprises aspects of viral oncology, quantitative image processing, transcriptomics, and stochastic modeling, aims to understand the key predictors of these observed fates at the single-cell level across infected B cell populations. Elliott’s prior research experience includes projects in biophysics, biomedical imaging, nanofabrication, and device engineering in both academic and industry settings.

Email: elliott.sorelle@duke.edu