Seminar on "Quantifying Long-Term Scientific Impact"

The University of Vermont Complex Systems Center is pleased to present, Dashun Wang, Assistant Professor of Information Sciences and Technology at the Pennsylvania State University and Adjunct Assistant Professor of Physics at Northeastern University.

http://www.dashunwang.com/

"Quantifying Long-Term Scientific Impact"
Monday, April 20, 2015
1:00 - 2:00pm
Kalkin Bldg, Room 004
Coffee and Dessert will be served

Abstract: The lack of predictability of citation-based measures frequently used to gauge impact, from impact factors to short-term citations, raises a fundamental question: Is there long-term predictability in citation patterns? Here, we derive a mechanistic model for the citation dynamics of individual papers, allowing us to collapse the citation histories of papers from different journals and disciplines into a single curve, indicating that all papers tend to follow the same universal temporal pattern. The observed patterns not only help us uncover basic mechanisms that govern scientific impact but also offer reliable measures of influence that may have potential policy implications.

Bio: Dashun Wang is Assistant Professor of Information Sciences and Technology at the Pennsylvania State University and Adjunct Assistant Professor of Physics at Northeastern University. Prior to joining Penn State, he was a Research Staff Member at the IBM T.J. Watson Research Center. Dashun received his PhD in Physics in 2013 from Northeastern University, where he was a member of the Center for Complex Network Research. His PhD advisor was Albert-László Barabási. From 2009 to 2013, he had also held an affiliation with Dana-Farber Cancer Institute, Harvard University as a Research Associate. He received his B.S. degree in Physics from Fudan University in 2007.

His research takes a multidisciplinary approach—combining his background in statistical physics, computer science, and computational social science—to exploit the opportunities and promises offered by Big Data. Through the lens of new and increasingly available large-scale datasets, he hopes to use and develop tools of network science to help improve the way in which we understand complexity and discover the underlying principles governing self-organized systems. His work has been applied to understand and predict social interactions, human mobility, knowledge production and scientific impact. His research has been published in both general audience journals and top computer science venues, and has been featured in Nature, Science, MIT Technology Review, The Economist, The Boston Globe, ORF, Physics World, among other outlets.