The Statistical and Applied Mathematical Sciences Institute (SAMSI) announces the two programs for the 2013–14 year will be Computational Methods in Social Science, and Low-Dimensional Structure in High-Dimensional Systems. The announcement was made by Richard Smith, Director of SAMSI.

Computational Methods in Social Science

The social sciences have, as many areas of research, experienced a data explosion. Social scientists are examining statistical and computational methodology more these days for handling social science datasets. Many statisticians and applied mathematicians are also focusing on social sciences in applications of their work, including looking at social networks and causal inference. The program will focus on three major areas: 1) Social Networks; 2) Agent-Based Models and 3) New Methodology for Censuses and Surveys.

The program leaders for the Computational Methods in Social Science are: Robert Axtell (George Mason University), Elena Erosheva (University of Washington), Doyne Farmer (Institute for New Economic Thinking, Oxford University, and Santa Fe Institute), Steve Fienberg (Carnegie Mellon University), Krista Gile (U Mass Amherst), Mark Handcock (UCLA), Tian Zheng (Columbia University). Richard Smith is the Directorate Liaison.

Low-Dimensional Structure in High-Dimensional Systems

The program on 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. The 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 during the program will include theoretical discussions of these tools as well as applications to image and signal analysis, graphs and networks, genetics and genomics, dynamical systems, and machine learning.

The program leaders for the LDHD program are: Florentina Bunea (Cornell), Peter Hoff (Washington), Chris Holmes (Oxford), Peter Kim (Guelph), Vladimir Koltchinskii (Georgia Tech), John Lafferty (U Chicago), Gilad Lerman (Minnesota), Sara van de Geer (ETH Zürich), Marten Wegkamp (Cornell) and Bin Yu (Berkeley). Ezra Miller is the Directorate Liaison.

Get involved!

There are several opportunities for IMS members to participate in either of these two programs. Financial support is available for visiting researchers to be resident at SAMSI for periods of one month to one year. Young researchers have special opportunities to participate that typically have a one year appointment. Several postdoctoral positions will also be funded for each SAMSI program. Workshops and working groups give many people the opportunity to collaborate with others on research projects and to 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 get chances to broaden their interests and skill sets, participate in cutting edge interdisciplinary projects and make new connections. New researchers and members of underrepresented groups are especially encouraged to participate in SAMSI workshops and programs.

To find out more about either of these research programs, or to apply, go to the SAMSI website,