The advances in sensing, storage, and networking technology have created huge collections of high-volume, high-dimensional data. Making sense of these data is critical for companies and organizations to make better business decisions, and brings convenience to our daily life. Recent advances in data mining, machine learning, and applied statistics have led to a flurry of data analytic techniques that typically require an iterative refinement process. However, the massive amount of data involved and potentially numerous iterations required make performing data analytics in a timely manner challenging. In this talk, we present a series of data parallel frameworks that accelerate iterative machine learning algorithms for massive data.

Lixin Gao is a University Distinguished Professor of Electrical and Computer Engineering at the University of Massachusetts at Amherst. She received a Ph.D. degree in Computer Science from the University of Massachusetts at Amherst. Her research interests include online social networks, and Internet routing, network virtualization and cloud computing. Between May 1999 and January 2000, she was a visiting researcher at AT&T Research Labs and DIMACS. She was an Alfred P. Sloan Fellow between 2003-2005 and received an NSF CAREER Award in 1999. She won the best paper award from IEEE INFOCOM 2010, and the test-of-time award in ACM SIGMETRICS 2010. Her paper in ACM Cloud Computing 2011 was honored with “Paper of Distinction”. She received the Chancellor’s Award for Outstanding Accomplishment in Research and Creative Activity in 2010, College of Engineering Outstanding Senior Faculty Award in 2013, and Outstanding Achievement in Research Award by College of Information and Computer Sciences at the University of Massachusetts in 2015.  She is a fellow of IEEE and ACM.