Data-Intensive Scientific Discovery in the Big Data Era

Speaker:  James Faghmous, University of Minnesota

Data science has become a powerful tool to extract knowledge from
large data. However, despite massive data growth in the sciences, it
remains unclear whether Big Data methods can lead to scientific
breakthroughs. I will introduce a new knowledge discovery paradigm --
theory-guided data science -- that brings together novel data analysis
methods and powerful scientific theory to extract knowledge from
complex spatio-temporal data. The principles of this paradigm will be
demonstrated with data mining applications to cluster dynamic
spatio-temporal data, analyze brain fMRI data, and monitor global
ocean dynamics.

Bio:

James Faghmous is a Research Associate at the University of Minnesota
where he develops new data science methods for data-intensive
scientific discovery. In 2015, James received an inaugural NSF CRII
Award for junior faculty and his doctoral dissertation received the
"Outstanding Dissertation Award" in Science and Engineering at the
University of Minnesota.

James received his Ph.D. from the University of Minnesota in 2013
where he was part of a 5-year $10M NSF Expeditions in Computing
project to understand climate change from data.  He graduated Magna
Cum Laude in 2006 from the City of College of New York where he was a
Rhodes and a Gates Scholar nominee.

For more information, please contact Andy Nealen.