News Brief: Aarti Singh Receives NSF CAREER Award-Carnegie Mellon News - Carnegie Mellon University

Tuesday, April 9, 2013

News Brief: Aarti Singh Receives NSF CAREER Award

Aarti SinghAarti Singh, assistant professor of machine learning, has received a Faculty Early Career Development (CAREER) Award from the National Science Foundation to develop computationally efficient and principled methods of extracting clusters and graphs from "big and dirty" data sets.

The work could have major impact on applications that involve grouping similar variables and learning complex interactions between them, including those in neuroscience and healthcare. For instance, accurately mapping neural pathways will help diagnose and treat brain pathologies at an early stage, and help understand brain functioning. Likewise, clustering patients and discovering disease-spreading pathways based on few measurements of relevant genetic features or indicators could help prevent and cure diseases, and also minimize healthcare costs.

The CAREER program offers the NSF's most prestigious awards for junior faculty. The awards support faculty members who exemplify the role of teacher-scholars through outstanding research, excellent education and the integration of education and research within the context of the mission of their organizations.

The results of Singh's project, "Distilling information structure from big and dirty data: Efficient learning of clusters and graphs in modern datasets," will enable efficient learning of clusters and graphs from data that is large-scale, high-dimensional, under-sampled, corrupted, and often only available in a compressed or streaming representation. It will provide a precise characterization of the tradeoffs between number of measurements, computational complexity and robustness in these settings, and how intelligent adaptive sampling can help improve these tradeoffs.

Singh received a bachelor's degree in electronics and communication engineering from the University of Delhi in 2001, and master's and Ph.D. degrees in electrical and computer engineering from the University of Wisconsin-Madison in 2003 and 2008, respectively. Prior to joining CMU's Machine Learning Department in 2009, she was a postdoctoral research associate at the Program in Applied and Computational Mathematics at Princeton University.