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Data Science with Context: Learning from Contextual Information in Climate and Health

Speaker: Dr. James Faghmous
Icahn School of Medicine - Mount Sinai

March 29, 2016 - 4:00 p.m. to 5:00 p.m.
Location: Troy 2018
Hosted By: Dr. Mohammed Zaki (x6340)


Data science has become a powerful tool to extract knowledge from the large data. However, despite massive data growth in the sciences, it remains unclear whether Big Data can lead to scientific breakthroughs. For example, data quality remains a challenge for generalizable learning in numerous settings. In this talk, I will show how leveraging the context of data, events, and phenomena enables us to overcome persistent data challenges such as missing values, large-scale variability, and measurement errors. The principles introduced in this talk will be demonstrated with a data mining application to monitor several Earth Science phenomena on a global scale. Finally, I will conclude by highlighting some of our projects and challenges in global health at the Arnhold Institute.


James Faghmous is an assistant professor and founding Chief Technology Officer of the Arnhold Institute for Global Health at the Icahn School of Medicine - Mount Sinai, where he develops new data science methods for modeling and designing more equitable health systems. 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 College of New York where he was a Rhodes and a Gates Scholar nominee.

Last updated: March 22, 2016