Exponential increases in computing power and falling costs have had a profound impact on the development of statistics and applied mathematics, particularly nonparametric modeling. The field of nonparametric and semi-parametric modeling has experienced extraordinary growth in the last two decades, thanks to significant advances in statistics, applied mathematics and computer science. Many data-analytic techniques have been developed and many new phenomena have been unveiled. They have become indispensible tools in contemporary statistics and data analysis. With such exciting developments and advancements, nonparametric techniques have played important and critical roles in many scientific disciplines such as genomics, biomedical studies, financial economics and machine learning.

The meeting will focus on developments of advanced nonparametric and semiparametric methods for complex biological data. These include high-dimensional data as in genomics and microarrays and associated problems of normalization, large n small p situations and dimension reduction, image data such as fMRI brain images, and functional data. Functional data arise in longitudinal studies, samples of growth curves, trajectories of reproduction, time-dynamic microarray expression trajectories, and other complex time-varying responses of biological systems.

The complexity of such biological data require new nonparametric/semiparametric approaches that are flexible, scale up to large data and enable a synthesis between different approaches such as functional and longitudinal methodologies. The meeting will focus on current developments and avenues of future research in nonparametric modeling, data-analytic methods and theory for these challenges.