Samuel Madden, MIT CSAIL on "Outlier and Data Debugging"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Samuel Madden is a Professor of Electrical Engineering and Computer Science in MIT's Computer Science and Artificial Intelligence Laboratory. His research interests include databases, distributed computing, and networking. Research projects include the C-Store column-oriented database system, the CarTel mobile sensor network system, and the Relational Cloud "database-as-a-service". Madden is a leader in the emerging field of "Big Data", heading the Intel Science and Technology Center (ISTC) for Big Data, a multi-university collaboration on developing new tools for processing massive quantities of data. He also leads BigData@CSAIL, an industry-backed initiative to unite researchers at MIT and leaders from industry to investigate the issues related to systems and algorithms for data that is high rate, massive, or very complex.

Madden received his Ph.D. from the University of California at Berkeley in 2003 where he worked on the TinyDB system for data collection from sensor networks. Madden was named one of Technology Review's Top 35 Under 35 in 2005, and is the recipient of several awards, including an NSF CAREER Award in 2004, a Sloan Foundation Fellowship in 2007, best paper awards in VLDB 2004 and 2007, and a best paper award in MobiCom 2006. He also received a a "test of time" award in SIGMOD 2013 (for his work on Acquisitional Query Processing in SIGMOD 2003), and a ten year best paper award in VLDB 2015 (for his work on the C-S

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Outlier and  Data Debugging

Rapidly developing areas of information technology are generating massive amounts of data. Human errors, sensor failures, and other unforeseen circumstances unfortunately tend to undermine the quality and consistency of these datasets by introducing outliers -- data points that exhibit surprising behaviour when compared to the rest of the data. In this talk I’ll describe some recent tools we’ve built at MIT CSAIL to identify these outliers in data, including a tool called AutoOD designed to automate many aspects of adapting existing outlier detection methods to complex datasets.