The Statistical and Applied Mathematical Sciences Institute, (SAMSI) announces its 2014-2015 programs. SAMSI’s programs will integrate applied mathematicians and statisticians with other scientific disciplines to further research in bioinformatics and ecology.

One program, Beyond Bioinformatics, Statistical and Mathematical Challenges, will look at the statistical and mathematical challenges arising in the analysis of genomic and related data with the goal of addressing relevant biological questions. As genomic and related data are growing more complex, novel methods need to be developed to help with data synthesis and analysis to answer previously inconceivable questions about biological processes. This program will focus on:
1) Statistical pre-processing of emerging high throughput data;
2) Dependence in high-dimensional data; in particular, multivariate discrete counts;
3) Integration of multi-omics data;
4) Modeling dynamics of mixtures, such as populations of cells, variants and meta-genomics; and
5) Big data and machine learning for addressing ‘omic issues.

Program leaders for “Beyond Bioinformatics” include: Alexander Alekseyenko, NYU School of Medicine; Karin Dorman, Iowa State University; Nick Hengartner, Los Alamos National Lab; Susan Holmes, Stanford University; Katerina Kechris, University of Colorado-Denver; Shili Lin, The Ohio State University; Dan Nettleton, Iowa State University and Hongyu Zhao, Yale University.

The other SAMSI program is Mathematical and Statistical Ecology. This program brings together three groups of researchers—statisticians, mathematicians and theoretical ecologists—to study and develop the interactions among different approaches that ecological modeling has developed. One approach is that theoretical ecologists have developed mathematical models that are analyzed using traditional tools of applied mathematics, such as partial differential equations (PDEs) and dynamical systems. These models are then used to look at resilience, tipping points or other ecological properties. A second approach, typically used by statisticians and data analysts, involves sophisticated statistical tools such as Bayesian hierarchical models that are applied to large spatio-temporal datasets, but often these models are developed without the detailed consideration of nonlinear dynamics. Some of the topics that will be explored through the year include:
1) Critical thresholds and tipping points;
2) Resilience of ecological systems; leading indicators;
3) Multi-scale and multivariate statistical method;
4) Climate and Biodiversity;
5) Implications for public policy.
There is also likely to be a joint working group between the two programs, on the topics of Landscape Genomics.

Program leaders for “Mathematical and Statistical Ecology” include: Philip Dixon of Iowa State University, Lou Gross of the University of Tennessee and NIMBioS, Jennifer Hoeting of Colorado State University, Mevin Hooten of Colorado State University, Lea Jenkins of Clemson University, Claire Lunch of the National Ecological Observatory Network, Ron McRoberts of the US Forest Service, Jay Ver Hoef of NOAA, and Linda Young of the National Agricultural Statistics Service.

There are many opportunities for people to be involved with the SAMSI programs. Financial support is available for visiting researchers to be resident at SAMSI for periods of one month to one year. Several postdoctoral positions are funded for each SAMSI program. Young researchers have special opportunities to participate that typically have a one year appointment. 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,