When: April 13, 2017
Who: Naren Ramakrishnan, PhD
Where: WWF’s Washington, D.C. Headquarters (1250 24th St. NW, Washington, DC 20037)
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About the seminar:
This talk will aim for an introduction to applied machine learning, big data analytics, and their applications to large societal problems. First, we will introduce the idea of machine learning on massive datasets and how it can accelerate the scientific discovery process and provide insight into a range of problems far greater than classical approaches. Next, we will detail our experiences developing a system (EMBERS) to forecast events such as protests, disease outbreaks, mass migrations, elections, domestic political crises – using a multitude of open source data feeds. Over the past three years, EMBERS has successfully forecast many international (and rare) events such as the “Brazilian Spring” (June 2013), Hantavirus outbreaks in Argentina and Chile (2013), student-led protests in Venezuela (Feb 2014), protests stemming from the kidnappings and killings of student-teachers in Mexico (Sep- Oct 2014), and protests in Paraguay (Feb 2015) against a new public-private partnership law. Finally, through our experiences operating EMBERS for 4 years, we will present how such a system can be developed and deployed to problems faced by conservationists, in areas such as illegal logging, deforestation, and climate security.
About the speaker:
Naren Ramakrishnan is the Thomas L. Phillips Professor of Engineering at Virginia Tech. He directs the Discovery Analytics Center, a university-wide effort that brings together researchers from computer science, statistics, mathematics, and electrical and computer engineering to tackle knowledge discovery problems in important areas of national interest. His work has been featured in the Wall Street Journal, Newsweek, Smithsonian Magazine, PBS/NoVA Next, Chronicle of Higher Education, and Popular Science, among other venues. Ramakrishnan serves on the editorial boards of IEEE Computer, ACM Transactions on Knowledge Discovery from Data, Data Mining and Knowledge Discovery, IEEE Transactions on Knowledge and Data Engineering, and other journals. He received his PhD in Computer Sciences from Purdue University.