Faculty

Christopher Williams, Ph.D.
Department Chair and Professor
Affiliate Professor of Bioinformatics and Computational Biology
My research interests are on problems in statistical genetics, biostatistics, and statistical methods applied to issues in natural resources. One of the topics that I work on is the analysis of human twin data. Another area of interest is the estimation of disease prevalence from various types of data, such as in groups of fish that are collected and have their tissue pooled to test for disease status.
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Erkan O. Buzbas
Assistant Professor
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.
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Brian Dennis
Professor
Research interests: Statistical Ecology, Biometrics, Mathematical Modeling, Theoretical Ecology, Conservation Biology, Population Dynamics
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Paul Hohenlohe, Ph.D.
Assistant Professor
Our research focuses on the genomic architecture of evolving populations, developing sophisticated theory and analytical tools to harness the power of modern DNA sequencing technology. We address basic questions of evolutionary biology as well as applications to conservation and cancer biology.
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Timothy R. Johnson, Ph.D.
Associate Professor, Affiliate Professor of Psychology
Currently, most of my research concerns ordinal variables and random utility models for categorical data. More broadly, I am interested in categorical data, latent variable models, measurement error models, simulation-based computations for inference, and Bayesian statistics. I sometimes collaborate in research in judgment and decision making.
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Paul Joyce, Ph.D.
Dean of College of Science & Professor
My research focuses on developing and rigorously testing statistical methods and stochastic models to describe genetic phenomena. These include models and methods to: predict how viruses adapt; show the effect of antibiotic resistance genes encoded on plasmids; predict ancestral relationships among species; and to understand the ecological structure of bacterial communities in biofilms. This broad focus has lead to collaborations with researchers in phylogenetics, population genetics, theoretical ecology, mircobial ecology, experimental evolution, conservation genetics, and the list is growing.
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Stephen Sauchi Lee, Ph.D.
Associate Professor
My general research area is Multivariate and Computational Statistics. It includes: Integrating models and methods from statistics, neural networks, machine learning, and data mining communities to discover relationships and recognize patterns in databases; modeling using regression and classification for interpretation and forecasting; extracting information and patterns; and developing computational algorithms to increase efficiency and prediction accuracy.
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Bahman Shafii
Director of Statistical Programs and Professor

(208) 885-9498 | bshafii@uidaho.edu
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Michelle Wiest, Ph.D.
Assistant Professor
Michelle’s research interests lie in epidemiological and biostatistical methods. Her work includes developing multivariate diagnostic and prognostic tools for evaluation of metabolic status, study design for nutrition interventions, and evaluation of risk factors for mining injuries. She plays a large role in supporting research across UI as a statistical consultant and in training master’s level statistics students in consulting.
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