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Erkan O. Buzbas

Erkan O. Buzbas

Associate Professor

Office

Brink Hall 415

Phone

208-885-4137

Mailing Address

Department of Mathematics and Statistical Science
University of Idaho
875 Perimeter Drive, MS 1103
Moscow, ID 83844-1103

  • 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

  • Theoretical Population Genetics
  • Meta-Research
  • Computational Methods
  • Bayesian Statistics

After completing my PhD with Prof. Paul Joyce at the University of Idaho, I worked at the University of Michigan and at Stanford University as a postdoctoral fellow under the supervision of Prof. Noah Rosenberg. My work on theoretical population genetics and meta-research are interdisciplinary and I collaborate with faculty from natural, social, mathematical sciences, and humanities.

  • 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 work in theoretical population genetics involves developing mathematical models of evolution at the population level and computational statistical methods to perform inference from these models. A particular focus area has been devising statistical methods that are scalable to complex models. My work in meta-research involves creating a meta-scientific framework to gain insight into the process of scientific discovery and issues surrounding reproducibility in science. This is a long-term research program that will investigate the process of scientific discovery from diverse perspectives, using modern philosophy of science and statistics, mathematical modeling, and computational methods.

Contact

Physical Address:
Brink Hall 415A

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

Phone: 208-885-2929

Fax: 208-885-7959

Email: stat@uidaho.edu

Web: Department of Statistical Science