Machine Learning Methods and Large Informatics Graphs

Principal Investigator(s): 
Michael Mahoney

In this project, researchers are tackling several problems with machine learning methods and large informatics graphs. First, they are looking at local algorithms and locally-biased algorithms, specifically extending local algorithms to other objective functions and the characterization of statistical properties of local algorithms. Second, they are scaling the algorithms up to larger networks, focusing on scaling up strongly-local and locally-biased methods and implementations on graphs that do not fit into RAM. The third problem is the application of data analysis to genetics and medical imaging data. The three specific genetic and medical research tasks they are focusing on are ancestry inference and disease discrimination in population genetics, patient classification based on fMRI data, and improved methods for construction of graphs and network data for medical data.