Data and Network Sciences: Challenges and Opportunities
Georgios Giannakis, PhD, Professor of Electrical and Computer Engineering at the University of Minnesota, Director of the Digital Technology Center
Monday, November 18, 2019, at 3:30 p.m.
Donna E. Shalala Student Center, Room 302
1300 Miller Drive
Coral Gables, FL 33146
Abstract
We live in an era of data deluge. Pervasive sensors collect massive amounts of information on every bit of our lives, churning out enormous streams of raw data in various formats. Mining information and learning from unprecedented volumes of data promises to limit the spread of epidemics and diseases, identify trends in financial markets, learn the dynamics of emergent social-computational systems, and also protect critical infrastructure including the smart grid and the Internet’s backbone network. While Big Data can be definitely perceived as a big blessing, big challenges also arise with large-scale datasets. This talk will overview challenges and opportunities emerging in the analytical and algorithmic foundations that are widely referred to as Data Science, and Network Science, the latter for data residing on graphs formed by agents that are interconnected (or networked) either physically or through their interdependencies.
- Yu Ding, PhD, is the Mike and Sugar Barnes Professor of Industrial & Systems Engineering, Professor of Electrical & Computer Engineering, and a member of Texas A&M Institute of Data Science, Texas A&M Energy Institute, and TEES Institute of Manufacturing Systems. Dr. Ding received his Ph.D. degree from the University of Michigan in 2001. Dr. Ding’s research interest is in the area of system informatics, and data and quality science. Dr. Ding is a recipient of the 2018 Texas A&M Engineering Research Impact Award, the recipient of the 2019 IISE Technical Innovation Award, and a Fellow of IISE and ASME. He recently published the book “Data Science for Wind Energy.”
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