Field Faculty

The PhD Program in Computational Biology is currently comprised of 25 graduate students and 26 field faculty from an array of research areas. We encourage you to explore the field faculty to get a better understanding of what is offered in the CB PhD Program.

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Computational genetics; Functional genomics of plant interactions with plant pathogenic bacteria; Role of TAL effects in plant disease and applications for disease control and DNA targeting, including genome editing

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Statistical topics ranging from the bootstrap and Monte Carlo methods, clustering, exact inference, mixed models, generalized linear models, applications of the saddlepoint and Laplace approximations and model fitting algorithms.

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Computational biology; Computational genetics; Genetics of adaptation and domestication; Bioinformatics

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 Population genetics, bacterial genomics, human microbiome, systems biology

ed buckler

Functional genomic approaches to dissect complex traits in grasses, cassava, and wide range of other crops; focused on combining the basic science of the molecular basis of natural variation with applied breeding

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My research interests are broadly in the epidemiology of zoonotic diseases, evidence-based medicine, and One-Health. Recently, I have been developing methods to improve antimicrobial resistance surveillance and track multidrug resistance. I use classical statistical methods, mathematical modeling, and machine learning to answer research questions.

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Molecular and theoretical population genetics; molecular evolution; Drosophila evolutionary biology

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Computational neuroscience, olfactory sensory processing, neural coding and representation; learning and memory systems, neuromorphic algorithms

Biomedical Sciences; gene regulation, functional genomic "next generation" sequencing data

Precision medicine; single-cell genomics; circulating cell-free DNA; genomic medicine

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Plant disease sensing; asymptomatic disease detection; remote sensing; proximal sensing; in situ and imaging spectroscopy; machine & deep learning

Basic metabolism in ruminants and genetics to veterinary epidemiology, economic modeling and food safety

Computational and experimental approaches to understand the structure of metabolic networks

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DNA replication timing programs using applied genomic approaches, integrating experimental and computational biology

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Quantitative neuroimaging of neurological disorders, including multiple sclerosis, stroke, traumatic brain injury and disorders of consciousness

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Epigenomic approaches to understanding the mechanisms of gene regulation in eukaryotic systems. We focus on combining genomics with bioinformatic algorithm development to deconvolute the eukaryotic regulatory network.

Neuroscience, neural coding, neuromodulation, olfactory sensory processing, and the neural bases of learning and memory processes

Gene expression, gene regulation, molecular biology, molecular genetics, Rna aptamers, Rna splicing, transcription

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Evolutionary genomics of rice

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Population genetics, computational evolutionary biology, evolutionary theory, genetics of rapid adaptation

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Quantitative genetics/genomics; statistical genetics; computational biology; pathway modeling; molecular evolution

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Plant biology, evolutionary genomics, computational biology, metabolomics, natural product discovery

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Systems biology, regulatory and signaling networks, biological information processing, and dynamics of complex systems

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Computational approaches that investigate gene regulation in yeast and human epigenomes

kelly robbins in bradfield lounge

Applying quantitative genetics, genomics and computational science to improve the efficiency of crop breeding programs and increase understanding of complex traits.

microRNA; gene regulation; genomics/bioinformatics; systems genetics; liver/GI stem cells; liver/GI disease; diabetes, dyslipidemia

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Cancer genetics/genomics with a strong focus on the identification and functional characterization of novel predisposing mutations in human cancers.

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Quantitative and experimental systems biology; statistical genetics; comparative genomics; machine learning; molecular evolution; disease prognosis analysis