Erkan O. Buzbas
College of Science
Campus Locations: Moscow
With UI Since 2012
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
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.