Understanding Climate Change: Opportunities and Challenges for Data Driven Research
Date: Wednesday, October 23, 2013
Time: 11:00 AM - 12:00
Venue: Research Hall 163
Speaker: Dr. Vipin Kumar
Talk Abstract:
Climate change is the
defining environmental challenge facing our planet,
yet there is considerable uncertainty regarding the
social and environmental impact due to the limited
capabilities of existing physics-based models of the
Earth system. This talk will present an overview of
research being done in a large interdisciplinary
project on the development of novel data driven
approaches that take advantage of the wealth of
climate and ecosystem data now available from
satellite and ground-based sensors, the
observational record for atmospheric, oceanic, and
terrestrial processes, and physics-based climate
model simulations. These information-rich datasets
offer huge potential for monitoring, understanding,
and predicting the behavior of the Earth's ecosystem
and for advancing the science of climate change.
This talk will discuss some of the challenges in
analyzing such data sets and our early research
results.
Short Biography:
Vipin Kumar is currently
William Norris Professor and Head of Computer
Science and Engineering at the University of
Minnesota. His research interests include High
Performance computing and data mining, and he is
currently leading an NSF Expedition project on
understanding climate change using data driven
approaches. He has authored over 250 research
articles, and co-edited or coauthored 10 books
including the widely used text book ``Introduction
to Parallel Computing", and "Introduction to Data
Mining" both published by Addison-Wesley. Kumar
co-founded SIAM International Conference on Data
Mining and served as a founding co-editor-in-chief
of Journal of Statistical Analysis and Data Mining
(an official journal of the American Statistical
Association). Kumar is a Fellow of the AAAS, ACM and
IEEE. He received the 2009 Distinguished Alumnus
Award from the Computer Science Department,
University of Maryland College Park, and 2005 IEEE
Computer Society's Technical Achievement Award for
his contributions to the design and analysis of
parallel algorithms, graph-partitioning, and data
mining. Kumar's foundational research in data mining
and its applications to scientific data 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).