Richard Smith, director of the Statistical and Applied Mathematical Sciences Institute (SAMSI), recently announced that the two programs for the 2013–2014 year will be Computational Methods in Social Science and Low-Dimensional Structure in High-Dimensional Systems.

Like many areas of research, the social sciences have experienced a data explosion. Social scientists are examining statistical and computational methodology more these days for handling social science data sets. Many statisticians and applied mathematicians also are focusing on social sciences in applications of their work, including looking at social networks and causal inference.

Computational Methods in Social Science will focus on social networks, agent-based models, and new methodology for censuses and surveys. Program leaders include Robert Axtell of George Mason University, Elena Erosheva of the University of Washington, Doyne Farmer of Oxford University and the Santa Fe Institute, Steve Fienberg of Carnegie Mellon University, Krista Gile of the University of Massachusetts-Amherst, Mark Handcock of the University of California at Los Angeles, and Tian Zheng of Columbia University. Smith is the directorate liaison.

Low-Dimensional Structure in High-Dimensional Systems (LDHD) is devoted to the development of methodological, theoretical, and computational treatment of high-dimensional mathematical and statistical models. Possibly limited amounts of available data pose added challenges in high dimensions. This program will address these challenges by focusing on low-dimensional structures that approximate or encapsulate given high-dimensional data. Cutting-edge methods of dimension reduction will be brought together from probability and statistics, geometry, topology, and computer science. These techniques include variable selection, graphical modeling, classification, dimension reduction in matrix estimation, empirical processes, and manifold learning. Working groups will include theoretical discussions of these tools and applications to image and signal analysis, graphs and networks, genetics and genomics, dynamical systems, and machine learning.

Program leaders for LDHD include Florentina Bunea of Cornell, Peter Hoff of the University of Washington, Chris Holmes of Oxford University, Peter Kim of Guelph, Vladimir Koltchinskii of Georgia Tech, John Lafferty of The University of Chicago, Gilad Lerman of the University of Minnesota, Sara van de Geer of ETH Zurich, Marten Wegkamp of Cornell, and Bin Yu of Berkeley. Ezra Miller is the directorate liaison.

There are several opportunities for people to participate in either of these programs. Financial support is available for visiting researchers to be residents at SAMSI for one month to one year. Young researchers have special opportunities to participate that typically have a one-year appointment. Several postdoctoral positions also will be funded for each program.

Workshops and working groups give many the opportunity to collaborate with others on research projects and network with their peers. Dedicated workshops will allow graduate and upper-level undergraduate students to learn about the latest research and applications in the statistical and mathematical sciences. All involved researchers will receive chances to broaden their interests and skill sets, participate in cutting-edge interdisciplinary projects, and make new connections. New researchers and members of under-represented groups are especially encouraged to participate.

Visit the SAMSI website for more information or to apply.