Past Research Programs
This year-long SAMSI program focuses on the emerging area of network science. This highly interdisciplinary field is characterized by novel interactions in the mathematical sciences occurring at the interface of applied mathematics, statistics, computer science, and statistical physics, as well as those areas with network-oriented thrusts in biology, computer networks, engineering, and the social sciences.
The 12-month SAMSI program will focus on the analysis of complex data types that are an extension of Functional Data Analysis where one considers methods to analyze data samples of complex objects. Modern science is generating a need to understand, and statistically analyze, populations of increasingly complex types. The term "Analysis of Object Data" (AOD) is aimed at encompassing a broad array of such methods. The proposed SAMSI program seeks to bring together a diverse group of researchers (from statistics, other parts of mathematics, and related sciences) to explore the common structure that underlies such methodologies, and to use this knowledge in turn to motivate and synthesize new approaches.
Pharmacokinetics (PK) is the study of the time course of drug concentration resulting from a particular dosing regimen. PK is often studied in conjunction with pharmacodynamics (PD). PD explores what the drug does to the body, i.e., the relationship of drug concentrations and a resulting pharmcological effect. Pharmacogenetics (PGx) studies the genetic variation that determines differing response to drugs. Understanding the PK, PD and PGx of a drug is important for evaluating efficacy and determining how best to use such agents clinically.
This 12 month SAMSI program is centered around the broad topic of Stochastic Dynamics with particular focus on analysis, computational methods, and applications of systems governed by stochastic differential equations. Two application areas will be emphasized: problems in biological sciences and dynamics of networks.
This 12 month SAMSI program will focus on problems encountered in dealing with random space - time fields, both those that arise in nature and those that are used as statistical representations of other processes. The sub-themes of environmental mapping, spatial epidemiology, and climate change are interrelated both in terms of key issues in underlying science and in the statistical and mathematical methodologies needed to address the science. Researchers from statistics, applied mathematics, environmental sciences, epidemiology and meteorology will be involved, and the program will promote the opportunity for interdisciplinary, methodological and theoretical research.
The goal of this program is stimulate collaborations between researchers in the psychometric and statistical communities. The desired outcome for the program will be a well-defined, concrete list of specific research directions that will facilitate methodological development in related psychometric/statistical models.
In recent years, methods from algebra, algebraic geometry, and discrete mathematics have found new and unexpected applications in systems biology as well as in statistics, leading to the emerging new fields of "algebraic biology" and "algebraic statistics." Furthermore, there are emerging applications of algebraic statistics to problems in biology. This year-long program will provide a focus for the further development and maturation of these two areas of research as well as their interconnections. The unifying theme is provided by the common mathematical tool set as well as the increasingly close interaction between biology and statistics. The program will allow researchers working in algebra, algebraic geometry, discrete mathematics, and mathematical logic to interact with statisticians and biologists and make fundamental advances in the development and application of algebraic methods to systems biology and statistics. The essential involvement of biologists and statisticians in the program will provide the applied focus and a sounding board for theoretical research
This 12 month SAMSI program will develop new approaches to scientific/statistical computing using innovative Sequential Monte Carlo (SMC) methods. The program will address fundamental challenges in developing effective sequential and adaptive simulation methods for computations underlying inference and decision analysis. The research will blend conceptual innovation in new and emerging methods with evaluation in substantial applied contexts drawn from areas such as control, communications and robotics engineering, financial and macro-economics, among others. Researchers from statistics, computer science, information engineering and applied mathematics will be involved, and the program will promote the opportunity for both methodological and theoretical research. The interdisciplinary aspects of the program are substantial, as is the attractiveness for students and postdocs.
This program comprises two weeks of research, mixing tutorials, research presentations and working group activities on the subject. The goal of this program is three-fold: 1) to bring the area to the attention of statistical researchers, whose expertise is critical to substantiate and clarify the necessary statistical theory and methodology; 2) to nurture the necessary interdisciplinary collaboration and communication between statistical researchers and statisticians who currently work or plan to work with basic and applied science researchers and 3) to provide an entry point into the field to interested students and faculty, and to allow researchers already specialized in the domain to exchange recent results and information.
This full-year SAMSI program will address fundamental issues in risk analysis and the linked problems associated with extreme events and decision theory.