IMPRINTS

摘要: Advances in technology and the ever-growing role of digital sensors and computers in science have led to an exponential growth in the amount and complexity of data that scientists collect. We are at the threshold of an era in which hypothesisdriven science is being complemented with data-driven discovery. This alternative way to pursue research is especially visible in modern biology, with the advent of genomics and the development of multiple imaging techniques to visualize living organisms at multiple time and length scales. The data collected are complex in size, dimension, and heterogeneity. These data provide unprecedented opportunities for new discoveries, but also come with challenges that need to be addressed. Solving those challenges requires expertise from multiple disciplines. There is a need to develop new mathematical models for formalizing the information content of data, and a need to develop novel, efficient algorithms for dimensionality/complexity reduction, tools for statistical analysis, as well as approaches to data exploration and visualization. The two workshops on “Frame Theory and Sparse Representation for Complex Data” and “Geometry and Shape Analysis in Biological Sciences” held amidst the program from 29 May to 16 June 2017 at the Institute for Mathematical Sciences aimed to illustrate and promote such an interdisciplinary framework. The workshops followed the full workflow of modern data analysis, including topics on advanced signal processing techniques for analyzing experimental data, topics on geometric and statistical data analyses, and applications to biological problems.