NSF CAREER award to improve data quality and data-driven processes
Alexandra Meliou, assistant professor of computer science at the University of Massachusetts Amherst, recently received a five-year, $549,996 National Science Foundation (NSF) Faculty Early Career Development award to design and develop new technologies that will assist scientists, data analysts and casual users in obtaining deeper insights about their data.
The CAREER award is one of NSF's most prestigious awards in support of junior faculty who exemplify the role of teacher-scholars through outstanding research, excellent education, and the integration of education and research.
"Today, data is critical in almost every aspect of society, including healthcare, education, economy, and science," says Meliou. "However, because data is easily shared and reused, it has become less curated and less reliable. Data is often misused because its validity and origin are unclear, and mistakes easily propagate as data is often used to derive other data."
Online repositories, marketplaces and other sharing platforms have facilitated the dissemination, availability and sharing of data. At the same time, data sources are becoming more diverse, more complex and more interconnected. In this new data-producing and data-sharing world, most data is not curated and data origin and derivation are often obscured, she explains.
Yet, now more than ever, applications are heavily data-driven and important decisions are made based on data, for example, what infrastructure should the government invest in, or who will get a mortgage. The decrease in data quality and increase in data reliance leads to a dangerous combination: poor understanding of data, data misuse and errors that result in significant financial costs to the United States and world economies.
Meliou's work identifies unique opportunities in deriving data insights by reasoning about the processes that operate on the data and about the transformations that data undergoes. "Applications take data as input and produce other data as output," says Meliou.
"Yet, typical data analysis methods examine data in a vacuum, ignoring the processes used to generate it." Meliou's work develops a new data analysis model that reasons about data derivations and reverse-engineers them to provide better explanations for unexpected observations, more accurate and efficient diagnoses of problems, and better tools for making data-driven decisions and planning. This work will improve the quality of data and of data-driven processes.
Meliou joined the UMass Amherst College of Information and Computer Science in 2012. She was previously a postdoctoral research associate at the University of Washington. She received her PhD in Computer Science from the University of California, Berkeley, in 2009. She was awarded a Google Faculty Research Award in 2013, and she is a 2008 Siebel Scholar. She has extensive service in program committee and organizational roles in the data management community and is most recently serving as a PC track chair for the ACM Special Interest Group on Management of Data (SIGMOD) 2016.
Provided by University of Massachusetts Amherst
