Abstract: 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.
Short Bio: 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.
Abstract: The era of a new definition of AI has arrived and it calls for a synergetic alliance of technologists, regulators and social-psychological scientists. Bell Labs is studying the limits of human cognition to define a new paradigm of Human-AI cognitive symbiosis which would amplify human intelligence at biological and cognitive levels. Exiting AI systems are still unable to amplify human capabilities into new cognitive levels. Our Augmented Human Cognition research program leverages the latest discoveries in physiological, neurological and psychological sciences along with our algorithmic AI knowledge to deeply inter-connect intelligent systems and human cognition to embark the ultimate human cognitive revolution. This talk will describe how new AI models for knowledge organization and presentation can improve critical decisions making, people general knowledge and more informed business strategies.
Short Bio: Alessandra Sala is the Head of Analytics Research at Nokia Bell Labs, the Technology Advisory Board Member at CeADAR and the Irish Ambassador of Women in AI. She has more than 10 years of experience in research and innovation, both in academia and industry, specifically on advanced analytics, customer experience, AI-based automation of cloud applications and machine learning for networks orchestration. Her track record of transferring innovation from research into business units was awarded in 2017 as ITP Innovator of the Year. She has strong experience with a wide range of telco products and systems while managing diverse teams in multiple locations. Her research focus lies on distributed algorithms, data analytics and complexity analysis with an emphasis on graph algorithms and recently AI, machine learning and deep learning.