Computational Models for the Spread of Human Populations

Professor Peter Z. Revesz
Department of Computer Science and Engineering
University of Nebraska-Lincoln
E-mail: revesz@cse.unl.edu
 

Abstract: Ancient human DNA can now be extracted from the Paleolithic and later times enabling answering questions about the spread of human populations. However, in order to answer some of the more difficult questions regarding the spread of human populations, the availability of ancient human DNA is not enough in itself. We also need smart and efficient data mining algorithms and computational models. In this talk we review and compare some data mining algorithms that have been proposed for modeling the spread of human populations. We also describe some examples of data mining that include data from Neolithic finds from present day Greece, Turkey and other Eastern and Central European countries.

Short biography: Peter Z. Revesz holds a Ph.D. degree in Computer Science from Brown University. He was a postdoctoral fellow at the University of Toronto before joining the University of Nebraska-Lincoln, where he is a professor in the Department of Computer Science and Engineering. Dr. Revesz is an expert in databases, data mining, big data analytics and bioinformatics. He is the author of Introduction to Databases: From Biological to Spatio-Temporal (Springer, 2010) and Introduction to Constraint Databases (Springer, 2002). Dr. Revesz held visiting appointments at the IBM T. J. Watson Research Center, INRIA, the Max Planck Institute for Computer Science, the University of Athens, the University of Hasselt, the U.S. Air Force Office of Scientific Research and the U.S. Department of State. He is a recipient of an AAAS Science & Technology Policy Fellowship, a J. William Fulbright Scholarship, an Alexander von Humboldt Research Fellowship, a Jefferson Science Fellowship, a National Science Foundation CAREER award, and a �Faculty International Scholar of the Year� award by Phi Beta Delta, the Honor Society for International Scholars..