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Erkan O. Buzbas

Erkan O. Buzbas

Assistant Professor

415 Brink Hall
(208) 885-4137
Mailing Address

Department of Statistical Science
University of Idaho
875 Perimeter Drive, MS 1104
Moscow, ID 83844-1104

Erkan O. Buzbas
  • B.S. Chemistry, Bogazici University, 2000.
  • M.S. Environmental Sciences, Bogazici University, 2003.
  • M.S. Statistics, University of Idaho, 2007
  • Ph.D. Bioinformatics and Computational Biology, University of Idaho
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  • Computational Statistics
  • Bayesian Statistics
  • Monte Carlo Methods
  • Statistical Population Genetics

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.

  • 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.

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.


Physical Address:
Brink Hall 415A

Mailing Address:
875 Perimeter Drive, MS 1104
Moscow, ID 83844-1104

Phone: (208) 885-2929

Fax: (208) 885-7959


Web: Department of Statistical Science