Student: David Mascharka, Undergraduate Student in Computer Science, Mathematics, and Philosophy, Drake University
Research Mentor: Christopher Porter
Classification and Compression of Cosmological Data Utilizing Kolmogorov Complexity
In recent years, researchers have utilized Kolmogorov complexity in the classification of various objects, based on normalized compression distance. 
The goal of this project is to apply these tools to the classification of cosmological phenomena such as categorizing types of stars (e.g. neutron stars, white dwarfs, and pulsars).
Kolmogorov complexity can be relativized to define the notion of mutual information between objects, which is useful in quantifying the similarity between them. With this notion, we can compare the amount of mutual information between members of a dataset in order to cluster the objects into their respective categories. This is precisely the machinery we will utilize in our approach.