Overview

Many issues in the health, medical, and biological sciences are addressed by collecting and exploring relevant data. The development and application of techniques to better understand such data are the fundamental concern of the Group in Biostatistics. The program offers training in the theory of statistics and biostatistics, the computer implementation of analytic methods, and opportunities to use this knowledge in areas of biological/medical research. The curriculum is taught principally by members of the Department of Statistics (College of Letters and Science) and the Division of Biostatistics (School of Public Health) and provides a wide range of ideas and approaches to the analysis of data.

Research Facilities

Graduate students in the group have direct access to a variety of specialized computing resources, as well as the services of the campus computing facilities. Research activity of the faculty currently includes biostatistical computing, statistical issues in AIDS research, survival analysis, environmental health, epidemiology, and statistical methods in genetics and computational biology. Projects in research areas provide opportunities for both practical experience and individual research. Cooperation with other departments allows unusually broad and effective training in both theoretical and applied directions.

Graduate Programs

Biostatistics: MA, MA/PhD, PhD

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Faculty and Instructors

+ Indicates this faculty member is the recipient of the Distinguished Teaching Award.

Faculty

Peter J. Bickel, Professor. Semiparametric models, asymptotic theory, hidden Markov models, bootstrap, empirical statistical models for genomic sequences .
Research Profile

David R. Brillinger, Professor. Risk analysis, statistical methods, data analysis, animal and fish motion trajectories, statistical applications in engineering and science, sports statistics.
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Deryk Van Brunt, Associate Clinical Professor. Information technology and communication systems to improve community and population health, Mobile and internet technologies and communication systems to support disease management, disease prevention, and health promotion Information technology and communication systems to improve policy.
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Perry De Valpine, Associate Professor. Population ecology, mathematical modeling and statistics.
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Sandrine Dudoit, Professor. Genomics, classification, statistical computing, biostatistics, cross-validation, density estimation, genetic mapping, high-throughput sequencing, loss-based estimation, microarray, model selection, multiple hypothesis testing, prediction, RNA-Seq.
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Mi-Suk Kang Dufour, Assistant Adjunct Professor. Implementation science emphasis on reproductive health, HIV and malaria programs, application of quantitative methods to the evaluation of population interventions in the effectiveness and scale up phases of implementation .
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Haiyan Huang, Associate Professor. Applied statistics, functional genomics, translational bioinformatics, high dimensional and integrative genomic/genetic data analysis, network modeling, hierarchical multi-lable classification.
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Alan Hubbard, Professor. Causal inference, Statistical issues in infectious disease, Bioinformatics .
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+ Nicholas P. Jewell, Professor. AIDS, statistics, epidemiology, infectious diseases, Ebola Virus Disease, SARS, H1N1 influenza, adverse cardiovascular effects of pharmaceuticals, counting civilian casualties during conflicts.
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Michael J. Klass, Professor. Statistics, mathematics, probability theory, combinatorics independent random variables, iterated logarithm, tail probabilities, functions of sums.
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Lexin Li, Associate Professor. Neuroimaging data analysis, networks data analysis, personalized recommendation, Statistical genetics, computational biology, Dimension reduction, variable selection, high dimensional regressions, Statistical machine learning, data mining, computational statistics .
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John Marshall, Professor in Residence. Mathematical models of infectious diseases, malaria epidemiology, dengue epidemiology, mosquito control, gene drive, population genetics.
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Rasmus Nielsen, Professor. Statistical and computational aspects of evolutionary theory and genetics.
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Maya Petersen, Associate Professor. Causal inference, Dynamic treatment regimes, HIV, Antiretroviral resistance .

Elizabeth Purdom, Associate Professor. Computational biology, bioinformatics, statistics, data analysis, sequencing, cancer genomics.
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Sophia Rabe-Hesketh, Professor. Biostatistics, educational statistics, latent variable models, multilevel models, generalized linear latent and mixed models, hierarchical models, longitudinal data, Item response models, structural equation models.
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John Rice, Professor. Analyzing data that arise in the form of random functions, such as time series, and which involve large quantities of data and computationally intensive analysis My recent work has centered around projects in astronomy.
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Corinne Riddell, Assistant Adjunct Professor. Racial and ethnic health inequalities, Social epidemiology, Perinatal epidemiology, Data visualization, Health variation across US states .
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+ Steve Selvin, Professor. Public health, biostatistics.
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Yun Song, Professor. Computational biology, population genomics, applied probability and statistics.
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Mark J. Van Der Laan, Professor. Statistics, computational biology and genomics, censored data and survival analysis, medical research, inference in longitudinal studies.
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Bin Yu, Professor. Practice, algorithm, and theory of statistical machine learning and causal inference .
Research Profile