Dr. Michael DeGiorgio is a statistical population geneticist whose research focuses on modeling and developing quantitative methods for understanding evolutionary history. His past projects have included constructing novel methods for assessing genetic diversity, modeling past human migration patterns using ancient and modern DNA, investigating signatures of natural selection in human populations, and designing algorithms for inferring relationships among species.

Over the years, Dr. DeGiorgio has made a number of important contributions to the development of statistical approaches for making inferences from genome-scale data. In particular, he designed the first model-based methods to scan genomes for balancing selection, constructed the first likelihood approach to correct for negative selection when identifying positive selection, developed a fast and statistically consistent method for inferring species phylogenies that is ideal for application to large genomic datasets, and co-led a study about demographic and adaptive history in the Americas that was the first to utilize population-level ancient DNA sequencing from a single population sampled over time.

Using his skills as a software engineer, Dr. DiGiorgio has also developed many software programs to perform various interrogations, classifications, and/or predictions related to genome-scale data using Python, R, and C++. Many of these programs can be viewed at http://degiorgiogroup.fau.edu.

Dr. DiGiorgio co-leads the Accenius data engineering team in predictive analysis research, data modeling, and quantitative methods analysis for genomics datasets. He holds a B.S. in both Mathematics and Computer Science from the University of Central Florida and earned his Ph.D. in Bioinformatics from the University of Michigan. His postdoctoral work focused on Population Genetics and was performed at the University of California, Berkley.