My main interest is in developing simulation-based computational statistical methods to perform model-based inference under complex models in life sciences.


research overview

model-based inference, computational statistics, Monte Carlo methods, simulation-based methods, approximate Bayesian computation, population genetics.
Project 01

research overview

My research involves stochastic modeling of evolutionary phenomena and development and applications of computational statistical methods to perform inference about evolutionary phenomena at the population level. I maintain a broad interest in statistical theory, philosophy of statistics and science, evolutionary theory, and complex systems. My current focus is 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. In theoretical population genetics, a topical interest is admixture to reconstruct the recent and ancient history of human populations.

a CV-ish

Project 02

a CV-ish

Professional Preparation
Bogazici University, Chemistry, B.S., 2000
Bogazici University, Environmental Sciences, M.S., 2003
University of Idaho, Statistics, M.S., 2007
University of Idaho, Bioinformatics and Computational Biology, Ph.D., 2009
University of Michigan, Human Genetics, Postdoctoral Fellow, 2009-2011
Stanford University, Biology, Postdoctoral Fellow, 2011-2012

Appointments
2013-present, Assistant Professor, Department of Statistical Science, University of Idaho
2009-2011, Postdoctoral Researcher, Human Genetics, University of Michigan
2011-2012, Postdoctoral Researcher, Biology, Stanford University

Synergistic Activities
Invited researcher hosted by National Museum of Natural History, Paris, France, 06/01/2014-07/31/2014.
Guest Lecturer for Computational Statistical Methods in Coalescent, BIOSTAT 683/883, University of Michigan, 2011. Prepared and presented a one-hour lecture on approximate Bayesian computation methods for graduate students, followed by discussion.
Preparation and maintenance of a website which keeps track of approximate Bayesian computational methods http://approximatebayesiancomputational.wordpress.com/ (~7,796 hits since June 2011). The website includes an introduction to approximate Bayesian computation, links to news and conferences in the field, and a list of comprehensive literature in the field. Free software development for: approximate Bayesian computational methods; for estimation of the strength of selection by Bayesian methods.

publications

peer-review and other work
Project 03

publications

Huang, L., Buzbas E.O., and Rosenberg N.A., 2013. A theoretical approach to imputation accuracy under the coalescent. Theoretical Population Biology 87: 62-74. pdf
Buzbas E.O., 2012. On “Estimating species trees using approximate Bayesian computation”. Molecular Phylogenetics and Evolution 65: 1014-1016. pdf
Joyce P., Genz A., and Buzbas E.O., 2012. Efficient simulation and likelihood methods for a class of non-neutral multi-allele models. Journal of Computational Biology 19: 650-661. pdf
Buzbas E.O., Joyce P., and Rosenberg N.A., 2011. Inference on balancing selection for epistatically interacting loci. Theoretical Population Biology 79: 102-113. pdf
Mosher J.T., Pemberton J.T., Harter K., Wang C., Buzbas E.O., Dvorak P., Simón C., Morrison S.J., and Rosenberg N.A., 2010. Lack of population diversity in commonly used human embryonic stem-cell lines. New England Journal of Medicine 362: 183-185. pdf
Buzbas E.O., Joyce P., and Abdo Z., 2009. Estimation of selection intensity under overdominance by Bayesian methods. Statistical Applications in Genetics and Molecular Biology 8: Article 32. pdf
Buzbas E.O. and Joyce P., 2009. Maximum likelihood estimates under the k-allele model with selection can be numerically unstable. The Annals of Applied Statistics 3: 1147-1162. pdf
Buzbas E.O. and Rosenberg N.A., 2013. AABC: approximate approximate Bayesian computation when simulating a large number of data sets is computationally infeasible. pdf
Garud N.R., Messer P.W., Buzbas E.O., Petrov D.A., 2013. Soft selective sweeps are the primary mode of recent adaptation in Drosophila melanogaster. pdf

ABC methods

I maintain a website on developments in approximate Bayesian computation.
Project 04

approximate Bayesian computation methods

I maintain a website to keep track of developments in approximate Bayesian computation (ABC) (a.k.a. likelihood-free), a class of computational statistical methods for Bayesian inference under intractable likelihoods. The site is meant to be a resource both for biologists and statisticians who want to learn more about ABC and related methods. The website includes recent arXived work as well as a comprehensive list of publications, ABC software, a short introduction to ABC for those who are unfamiliar with ABC methods, and announcements of meetings.

Institute for Bioinformatics and Evolutionary Studies

The Institute for Bioinformatics and Evolutionary Studies (IBEST) is an organization of faculty and their research programs at the University of Idaho that share an interest in evolutionary biology and computational biology. This organization includes a spectrum of activities. Research done under the umbrella of IBEST receives funding from various sources including NIH, NSF, other federal agencies and industry. IBEST provides a rich intellectual environment in which cross disciplinary, university wide research and education flourishes. We meet at least weekly for scholarly discussions and conversation, together with students and staff. Faculty members in IBEST include physicists, chemists, molecular biologists, organismal biologists, ecologists, behavioral biologists, mathematicians, statisticians, and computer scientists. IBEST also administers the Bioinformatics and Computational Biology graduate program, which requires students to engage in research and studies at the intersection of computer science, biology, mathematics, and statistics. Many IBEST faculty members also engage undergraduate students in research.

BCB

I am an affiliated faculty in the graduate program in Bioinformatics and Computational Biology.
Project 06

Bioinformatics and Computational Biology program

The most exciting research often lies at the interfaces of multiple disciplines. Interdisciplinary training is required to tackle the most cutting edge problems in genetics, genomics, population biology, mathematical modeling, statistics and computer science. Bioinformatics and Computational Biology is a highly flexible interdisciplinary graduate program (MS. and PhD.) that prepares students to conduct research in academics, health sciences, agriculture, and other industries. The BCB program integrates research and coursework in Computer Sciences, Biological Sciences, and Mathematical Sciences.

collaborators

Project 02

collaborators

Paul Hohenlohe, University of Idaho
Paul Joyce, University of Idaho
Philipp Messer, Stanford University
Craig Miller, University of Idaho
Trevor Pemberton, University of Manitoba
Dmitri Petrov, Stanford University
Noah Rosenberg, Stanford University
Paul Verdu, CNRS, (France)

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