Virgina Tech Student Wins Poster Award Using SuperDARN Data

By: Nathaniel Frissell on: Wed., Feb. 29, 2012 06:00 PM EST (8998 Reads)

Prithwish Chakraborty (third from left), a graduate student in the Virginia Tech Discovery Analytics Center, won an award for his poster on Gravity Wave Detection in SuperDARN Data.

A team of DAC students won poster awards to present and attend the Humboldt Kolleg on "Collaboration and Networks in the 21st Century", held Feb 24-25 in Arlington, VA. Featuring prominent speakers including Dr. Bruce Alberts (Editor-in-chief, Science), Dr. Rita R. Colwell (former director, NSF), Dr. Mark A. Suskin (Acting Deputy Director Office of Cyberinfrastructure, NSF), and Dr. Cathleen Fisher (President of the American Friends of the Alexander von Humboldt foundation) among others, the symposium stressed on the paramount importance of collaborative research in the modern world.

Prithwish Chakraborty, Samah Gad, and Patrick Butler presented their work on mining gravity waves, data mining in culturomics, and storytelling for text analytics, respectively:

P. Chakraborty, N. Frissell, N. Ramakrishnan, J.B.H. Baker, and J. Michael Ruohoniemi, Discovering Gravity Wave Signatures from the SuperDARN Network Using Spatio-Temporal Pattern Mining
S. Gad and N. Ramakrishnan, Data Mining Techniques for Culturomics
P. Butler, M. Shahriar Hossain, N. Ramakrishnan, and A.P. Boedihardjo, Storytelling in Entity Networks to Support Intelligence Analysts

The format of the kolleg provided many opportunities for synergy and collaborative discussions. Dr. Suskin made a special mention of EarthCube, a NSF initiative aimed at developing community-guided cyberinfrastructure to integrate data and information for knowledge management across the geosciences; the SuperDARN project and collaborators are prominent contributors to Earthcube. Recent advances made by DAC in culturomics also found an audience in the symposium with experts from the humanities.

(Story originally from http://dac.cs.vt.edu/node/67).

Virgina Tech Student Wins Poster Award Using SuperDARN Data

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