I am interested in mathematical and statistical modeling of complex systems in life sciences. I develop simulation-based computational statistical methods to perform inference under complex mechanistic models, and investigate the relationships between mechanistic and phenomenological models.


research

Project 01

research

My research involves stochastic modeling of population-level phenomena, and development and applications of computational statistical methods to perform inference at the population level. My main area of application has been population genetics due to the mathematical maturity of this field that allows developing mechanistic models. However, I maintain a broad interest in modeling of complex systems, statistical theory and developing methodology. 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.

Center for Modeling Complex Interactions

Project 02

Center for Modeling Complex Interactions

The Center for Modeling Complex Interactions (CMCI) Modeling improves research at all stages – hypothesis formulation, experimental design, analysis, and interpretation – and provides a natural language by which exchange of ideas can highlight commonalities and uncover unforeseen connections between problems. CMCI will create the intellectual, cultural, and physical environment to foster convergence in interdisciplinary biomedical research. It brings together empirical scientists and modelers to address problems across all levels of biological organization, from biophysical to ecological. The focal point of CMCI is the Collaboratorium, a space and a culture for collaborative modeling. Research groups focused on specific questions will interact with postdoctoral scientists who reside in the Collaboratorium and devote full-time effort to collaborative modeling. The first research focus of CMCI is viral co-infection. Co-infection is common in nature and yet understudied in the laboratory. CMCI will use two experimental systems to study viral co-infection, with complementary modeling from several fields.

publications

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. PLoS Genetics, DOI: 10.1371/journal.pgen.1005004. pdf

ABC

Project 04

approximate Bayesian computation methods

I maintain a website to keep track of developments in approximate Bayesian computation (ABC), 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.

CV

Project 05

CV

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 Talk. Department of Statistics, University of Virginia, 04/24/2015.
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.
Preparation and maintenance of a website which keeps track of approximate Bayesian computational methods http://approximatebayesiancomputational.wordpress.com/ (>10,000 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.

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)

Institute for Bioinformatics and Evolutionary Studies

Project 06

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.

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