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Department of Statistical Science
phone: (208) 885-2929
fax: (208) 885-7959
415A Brink Hall
875 Perimeter Drive, MS 1104
Moscow, ID 83844-1104

Erkan O. Buzbas

Erkan O. Buzbas

Office: 415 Brink Hall
Phone: (208) 885-4137
Email: erkanb@uidaho.edu
Mailing Address: UI-Statistics Department
875 Perimeter Dr. MS 1104
Moscow, ID 83844-1104

Statistical Science
Assistant Professor

Campus Locations: Moscow
With UI Since 2013

  • Research/Focus Areas
    • Computational Statistics
    • Bayesian Statistics
    • Monte Carlo Methods
    • Statistical Population Genetics
  • Biography

    After finishing my PhD, I was a postdoctoral fellow at the Department of Genetics, University of Michigan (2009-2011), and at the Department of Biology, Stanford University (2011-2012). My collaborators include my postdoctoral advisor Noah Rosenberg at Stanford University, Paul Verdu at CNRS--France, and my PhD advisor Paul Joyce at the University of Idaho.

  • Selected Publications
    • E.O. Buzbas. On 'Estimating species trees using approximate Bayesian computation', Molecular Phylogenetics and Evolution, 65: 1014-1016
    • Paul Joyce, Alan Genz and E.O. Buzbas. Efficient simulation methods for a class of nonneutral population genetics models. Journal of Computational Biology, 19: 650-661.
    • E.O. Buzbas, P. Joyce and N.A. Rosenberg. Inference on balancing selection for epistatically interacting loci. Theoretical Population Biology, 79: 102-113, Issue 3.
    • Mosher, J.T., Pemberton, J.T., Harter, K., Wang, C., Buzbas, E.O., Dvorak, P., Simón, C., Morrison, S.J. and Rosenberg, N.A. “Lack of population diversity in commonly used human embryonic stem-cell lines.” New England Journal of Medicine, 362: 183-185.
  • Research Projects

    My research involves theoretical modeling of evolutionary phenomena at the population level and development and applications of computational statistical methods to perform inference about evolutionary phenomena using population genetic data. I maintain a broad interest in statistical theory and philosophy of statistics, evolutionary theory, and complex systems. For more information please visit my personal website.

    Currently, I focus on inference under statistical models with computationally intractable likelihoods. In particular, I work on theory and applications of approximate Bayesian computation (ABC), a class of computational statistical methods to perform inference from models with computationally intractable likelihoods. I maintain a website to keep track of developments related to the ABC methods. The website is meant to be a resource both for biologists and statisticians who want to get familiar with ABC methods and contains a short introduction to ABC, meeting announcements, and a comprehensive list of publications.

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