Agent-Based Modeling and Simulation
Instructor: Dr. Tim Gulden
Center for Social Complexity and Department of Computational Social Science
George Mason University
This short course will provide an introduction to agent-based modeling. Topics covered will include the role of simulation in the social sciences, the place of agent-based modeling in the context of social simulation and its utility as a tool for developing understanding the causal mechanisms that produce aggregate results. We will review practical applications of agent-based modeling and explore the behavior of a selection of sample models. Finally, we will examine the NetLogo agent-based modeling environment and conduct a tutorial in basic NetLogo programming. At the end of the short course, participants should be familiar with the capabilities of the agent-based modeling paradigm and have the tools and resources to independently pursue further work in the area.
Biosketch for Dr. Tim Gulden
Timothy Gulden is a research assistant professor with the Center for Social Complexity in the Krasnow Institute for Advanced Study at George Mason University. His PhD is from University of Maryland School of Public Policy and his dissertation was entitled "Adaptive Agent Modeling in a Policy Context". He has been a visiting scholar at the Brookings Institution's Center for Social and Economic Dynamics (CSED) and attended the Santa Fe Institute's Complex Systems Summer School in 2002. From 1989 through 1999, he was the technical director of the GIS program for Westchester County, New York. His major project at present seeks to apply agent-based modeling techniques to the development of deeper understanding of civil violence in East Africa. His research interests range from methodological issues in the development and validation of agent-based models, through the development of methods for applying complex systems techniques and remote sensed data to furthering understanding the role of urban agglomerations in the process of globalization.
Statistical Natural Languages and Text Mining
Instructor: Edward J. Wegman
Departments of Computational and Data Sciences and of Statistics
George Mason University
This short course will discuss the basics of natural language processing and its application to text mining. We will begin with a discussion of the rationalist approach to language and compare it with the empiricist approach. This will lead to a discussion of statistical natural language processing. We will discuss some information theory foundations and describe linguistic essentials including morphology, semantics, syntax, lexicon and phrase structure. The course will then focus on decoding text, denoising, and stemming. Vector space models as well as other text coding models will be discussed. The range of text mining goals will be discussed and, in particular, some examples of text mining applications will be given. This short course will orient participants to some of the relevant literature and provide background to pursue further work in the area.
Biosketch for Edward J. Wegman
Professor Wegman received his B.S. in mathematics degree from St. Louis University in 1965. He received the M.S.
and Ph.D. degrees in mathematical statistics from the University of Iowa, the latter degree in 1968. Subsequently,
he spent 10 years on the faculty of the world-class Department of Statistics at the University of North Carolina.
Dr. Wegman's early career focused on the development of aspects of the theory of mathematical statistics. In 1978,
Professor Wegman went to the Office of Naval Research (ONR) where he was the Head of the Mathematical Sciences Division.
In this role, he had responsibility Navy-wide for basic research programs in applied mathematics, statistics and
probability, systems theory, operations research, discrete mathematics, communication theory, and numerical analysis and
computational architectures. In addition, he was responsible for a variety of cross-disciplinary areas including such
projects as mathematical models of biological intelligence, mathematical methods for remote sensing, and topological methods
in chemistry. Dr. Wegman came to George Mason University with an extensive background in both theoretical statistics and computing
technology, with an extensive knowledge of the considerable data analytic problems associated with large scale scientific and
technical databases, and with a strong motivation to develop the computational and methodological tools to address these problems.
In 1986, he launched the Center for Computational Statistics and developed the M.S. in Statistical Science degree program. He has
been involved with the development of the Ph.D. program in Computational Sciences and Informatics at George Mason University.
Dr. Wegman served in national office in the Institute of Mathematical Statistics, the American Statistical Association and
the American Association for the Advancement of Science. He has published more than 200 papers and nine books. He is
past Theory and Methods editor of the Journal of the American Statistical Association, has served as Chair of the National
Research Councilís Committee on Applied and Theoretical Statistics, and served on the Board of Directors of the American Statistical
Association. He is currently co-Editor-in-Chief of the award-winning Wiley Interdisciplinary Reviews: Computational Statistics.
His professional stature has been recognized by his election as Fellow of the American Statistical Association, the American Association
for the Advancement of Science, the Washington Academy of Science and the Institute of Mathematical Statistics.
In addition, he was elected as a Senior Member of IEEE. Dr. Wegman has been elected to membership in the International Statistical
Institute and the Research Society on Alcoholism. Dr. Wegman has also received numerous awards including the Navy's Meritorious
Civilian Service Medal, the Army Wilks Medal, the American Statistical Association Founderís Award, and the University of
Iowa Distinguished Alumni Achievement Award. Dr. Wegman is the Bernard J. Dunn Professor of Data Sciences and Applied Statistics,
the Founding Chairman of the Department of Statistics, and the Director of the Center for Computational Data Sciences.