**Research Interests and
Educational Opportunities**

My research
interests combine mathematical modeling and statistical analysis using
computationally intensive methods and simulation. My research stems from current problems in
biology. Interested graduate students,
undergraduate students and postdocs are encouraged to
contact me directly by e-mail at zabdo@uidaho.edu
for research opportunities in the areas listed below.

Descriptions of
some of the projects, interesting to me, follow:

**Statistical methods for the analysis of
microbial community composition:**

Diseases occur not
only due to harmful pathogens that act in isolation but also due to disruption
of the balance of the human-microbial ecosystem (the human microbiome).
This emerging knowledge requires a revision of the current diagnostic
approaches to incorporate information about the “normal” state of these
ecosystems and the nature of deviation from this state that can result in
disease. It also requires making these revised approaches readily available to
the medical community to facilitate diagnosing diseases. The goal for this
project is to develop computational approaches to differentiate between normal
and abnormal (associated with disease symptoms) microbial communities, taking
metadata (such as gender, age, ethnicity, and health history) into
consideration.

**Experimental Evolution (Plasmid and phage
Evolution):**

Mathematical
models provide predictive power to help explain the evolutionary and ecological
mechanisms of plasmids and phage. I am interested in developing stochastic
models to describe the mechanics of evolution and the interaction between a
parasite (a plasmid or phage) and a host (a bacterial cell) and utilize these
models in statistical inference of the factors most important in such
ecological and evolutionary structure.

**Barcoding****
of Life (A decision theoretic approach based on the coalescent):**

Accurate
assignment and clustering are crucial for responding effectively and
efficiently to newly detected potential disease carriers or disease causing
species. Assignment facilitates prediction of the biology of the classified
individual(s). Should we identify an insect to belong to a disease-carrying
species/group, for example, we could invoke counter measures to eliminate the
environmental conditions that help the spread of these insects. Accurate
assignment depends on the accurate and correct characterization of
groups/species, and, hence, on the accurate and correct clustering. I am
interested in developing new, model-based, statistical methods to use barcode
data to quickly and accurately identify (assign) individual organisms and to
distinguish and characterize (cluster) different species and groups. Methods I
develop utilize the evolutionary history, inferred from the data, and a measure
of similarity or difference, in a decision theoretic framework, to make an
informed decision of assignment or clustering.

**Systematic Biology (A decision theoretic
approach to model selection in phylogenetic
analysis): **

Likelihood and
Bayesian methods in phylogenetic analysis rely on
choosing a justifiable stochastic model of evolution based on which researchers
infer the relationship between different individuals (taxa)
that belong to different species. We
developed and continue to refine a decision theoretic approach that takes into
account performance, as well as fit in choosing an evolutionary model for phylogenetic inference.

The underlying
mathematics and statistics disciplines of the above described projects are:
Statistical Genetics, Bayesian Statistics, Mathematical Biology, Stochastic
Processes and Optimization