Twin Cities WRS Seminar


Big Data in Water: Opportunities and Challenges for Machine Learning

Speaker

Dr. Vipin Kumar
Dept. of Computer Science and Engineering

January 19, 2018
12:30pm

Abstract

NOTE - The first WRS Seminar of the Spring Semester will be a part of the Water Resources Assembly and Research Symposium and will be at 12:30pm instead of the usual 3pm.

Abstract:
Water resources worldwide are coming under stress due to increasing demand from a growing population, increasing pollution, and depleting or uncertain supplies due to changing climate in which drought and floods have both become more frequent. As domains associated with Water continue to experience tremendous data growth from models, sensors, and satellites, there is an unprecedented opportunity for machine learning to help address urgent water challenges facing the humanity. This talk will examine the role of big data and machine learning can play in advancing water science, challenges faced by traditional Machine learning methods in addressing the domain of water, and some early successes.

Bio:
Vipin Kumar is a Regents Professor and holds William Norris Chair in the Department of Computer Science and Engineering at the University of Minnesota. His research interests include data mining, high-performance computing, and their applications in Climate/Ecosystems and health care. He is currently leading an NSF Expedition project on understanding climate change using data science approaches. He has authored over 300 research articles, and co-edited or coauthored 10 books including the widely used textbook ``Introduction to Parallel Computing", and "Introduction to Data Mining". Kumar is a Fellow of the ACM, IEEE, AAAS, and SIAM. Kumar was honored by the ACM SIGKDD 2012 Innovation Award, which is the highest award for technical excellence in the field of Knowledge Discovery and Data Mining (KDD), and the 2016 IEEE Computer Society Sidney Fernbach Award, one of IEEE Computer Society's highest awards for contributions to high-performance computing.  Learn more.