Computational Biology & Bioinformatics

PHD in Computational Biology & Bioinformatics

Program Principles & Goals

The PhD Program in Computational Biology & Bioinformatics (CBB) is an integrative, multi-disciplinary training program that encompasses the study of biology using computational and quantitative methods. In and out of the classroom, students learn to apply the tools of statistics, mathematics, computer science and informatics to biological problems. The vibrant and innovative Duke research in these fields provides exciting interactions between biological and computational scientists. Because the Program in Computational Biology and Bioinformatics is based in the Duke Center for Genomic and Computational Biology, it offers a unique opportunity for students to become one of tomorrow's leaders in the genome sciences.

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  • In addition to the Center for Genomic and Computational Biology, I am affiliated with the Center for Systems Biology and I formerly directed the Program in Computational Biology and Bioinformatics. I have been at Duke since September 2001, when I received my Ph.D. from the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology under the supervision of David Gifford, Tommi Jaakkola, and Rick Young.

    Generally, my research interests are in computational systems biology and machine learning. Specifically, my work focuses on the development and application of new statistical learning algorithms to complex problems in systems biology.

    Although these problems are quite diverse, a number of common themes appear repeatedly throughout my work: probabilistic representations, Bayesian statistics, fusion of information from multiple sources, optimization of joint objective functions, and learning in high-dimensional spaces without over-fitting. Many of these themes are variations on two simple ideas: careful attention to biology in the development of statistical models and the use of informative Bayesian priors to both regularize and guide automated learning.</p>

    Computational biology, machine learning, Bayesian statistics, systems biology, transcriptional regulation, genomics and epigenomics, graphical models, Bayesian networks, computational neurobiology, classification, feature selection
    Research Interests
    • Transcriptional regulatory networks
    • Organization of chromatin
    • Protein-DNA binding
    • Cell cycle control
    • Bayesian statistical inference
    • Model-based interpretation of big data
5th year CBB Student
Nov 9
Luci Bai
Penn State, Departments of Biochemistry and Molecular Biology

Regulatory mechanism and functional significance of divergent gene pairs

Nov 16
Kirill Korolev
Boston University, Department of Physics

The tug-of-war between deleterious and beneficial mutations in cancer