NSF Big Data Initiative Awards
The National Science Foundation, with support from the National Institutes of Health, awarded two grants totaling more than $1.7 million to Carnegie Mellon researchers to support new big data research projects on Oct. 3.
Christos Faloutsos, a professor in the Machine Learning Department, received an $894,892 award to develop theory and algorithms to tackle the complexity of language processing, and to develop methods that approximate how the human brain works in processing language. Faloutsos' co-principal investigator is Professor Tom Mitchell of Machine Learning.
Aarti Singh, assistant professor in Machine Learning, was awarded $820,000 to develop new statistical and algorithmic machine learning approaches that would benefit the fields of physics, psychology, economics, epidemiology, medicine, and social network analysis. Singh's co-PIs are Timothy Verstynen, assistant professor of psychology in the Center for the Neural Basis of Cognition, and Barnabás Póczos, assistant professor in machine learning. Read the NSF announcement.
Paper highlighted in Journal of the American Statistical Association (JASA)
Qirong Ho, Ankur Parikh and Eric Xing of the Machine Learning Department propose “A Multiscale Community Blockmodel for Network Exploration” that allows investigators to infer these phenomena from a set of observed network interactions. Ho et al. develop a stochastic model for partitioning the units in the network, say species, in a hierarchically organized tree. Each species’ interactions are governed by a multiscale membership vector that describes the species likelihood of interacting with species at different levels of the hierarchical tree.
Finally, a probability model that links the hierarchical tree and the membership vectors to observed network connections can be used to infer the parameters of the model. The authors demonstrate the approach on a network describing the predator-prey relationships among a collection of 75 species of grass-feeding wasps and their parasites.
