A three dimensional bridge between computer science and biology
The molecular basis of life rests on the activity of biological macromolecules, mostly nucleic acids and proteins. A perhaps surprising finding that crystallized over the last handful of decades is that geometric reasoning plays a major role in our attempt to understand these activities.
In my presentation, I will explore this connection between biology and geometry, focusing on theoretical and algorithmic developments in the corresponding domain of biogeometry. More specifically, I will
- Review our own developments in mathematics and computer science on understanding the geometry of unions of balls, and their applications in bio-molecular studies
- Review our applications of geometry to analyse large (biological) data sets.
A large and complex collection of data, usually called a data cloud, naturally embeds multi-scale characteristics and features, generically termed geometry. Understanding this geometry is the foundation for extracting knowledge from data. I will present a new methodology, called data cloud geometry-tree (DCG-tree) to resolve this challenge.
Computing platforms for healthcare big data
A comprehensive review of different big data platforms are presented which can potentially help us in making the right decisions in choosing the platforms based on their data/computational requirements. We review the data processing platforms that are currently available and discuss the advantages and drawbacks for each of them. Several details on each of these hardware platforms along with some of the popular software frameworks such as Hadoop and Spark are also provided. A thorough comparison between different platforms based on some of the important characteristics (such as scalability and real-time processing) has also been made through star based ratings.