A
Aravind Srinivasan
Researcher at University of Maryland, College Park
Publications - 278
Citations - 14614
Aravind Srinivasan is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Approximation algorithm & Wireless network. The author has an hindex of 60, co-authored 266 publications receiving 13711 citations. Previous affiliations of Aravind Srinivasan include Graz University of Technology & Bell Labs.
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An Improved Approximation for $k$-median, and Positive Correlation in Budgeted Optimization
TL;DR: In this paper, the authors improve upon Li-Svensson's approximation ratio for the k-median problem from 2.732 + \epsilon to 2.675 + ǫ.
Proceedings ArticleDOI
'Beating the news' with EMBERS: forecasting civil unrest using open source indicators
Naren Ramakrishnan,Patrick Butler,Sathappan Muthiah,Nathan Self,Rupinder Paul Khandpur,Parang Saraf,Wei Wang,Jose Cadena,Anil Vullikanti,Gizem Korkmaz,Chris J. Kuhlman,Achla Marathe,Liang Zhao,Ting Hua,Feng Chen,Chang-Tien Lu,Bert Huang,Aravind Srinivasan,Khoa Trinh,Lise Getoor,Graham Katz,Andy Doyle,Ackermann Christopher F,Ilya Zavorin,Jim Ford,Summers Kristen M,Youssef Fayed,Jaime Arredondo,Dipak K. Gupta,David R. Mares +29 more
TL;DR: In this paper, the authors describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indicators, and other data sources.
Proceedings ArticleDOI
Improved distributed algorithms for coloring and network decomposition problems
TL;DR: It is shown that A-coloring G is reducible in 0(log3 n/log A) time to (A+ I)-vertex coloring G in a distributed model, which leads to fast distributed algorithms, and a linear–processor NC algorithm, for Acoloring.
Proceedings ArticleDOI
Distributions on level-sets with applications to approximation algorithms
TL;DR: This work considers a family of distributions on fixed-weight vectors in {0, 1}/sup t/ that enjoy certain negative correlation properties and also satisfy pre-specified conditions on their marginal distributions, and derives an approximation algorithm whose approximation guarantee is at least as good as what is known.
Proceedings ArticleDOI
Predicting Trust and Distrust in Social Networks
TL;DR: A new method for computing both trust and distrust (i.e., positive and negative trust) is presented by combining an inference algorithm that relies on a probabilistic interpretation of trust based on random graphs with a modified spring-embedding algorithm.