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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Journal ArticleDOI
Modelling disease outbreaks in realistic urban social networks.
Stephen Eubank,Hasan Guclu,V. S. Anil Kumar,Madhav V. Marathe,Aravind Srinivasan,Zoltán Toroczkai,Nan Wang +6 more
TL;DR: The results suggest that outbreaks can be contained by a strategy of targeted vaccination combined with early detection without resorting to mass vaccination of a population.
Journal ArticleDOI
Mobile Data Offloading through Opportunistic Communications and Social Participation
TL;DR: This work proposes to exploit opportunistic communications to facilitate information dissemination in the emerging Mobile Social Networks (MoSoNets) and thus reduce the amount of mobile data traffic.
Proceedings ArticleDOI
Splitters and near-optimal derandomization
TL;DR: A fairly general method for finding deterministic constructions obeying k-restrictions, which yields structures of size not much larger than the probabilistic bound and imply the very efficient derandomization of algorithms in learning, of fixed-subgraph finding algorithms, and of near optimal /spl Sigma/II/Spl Sigma/ threshold formulae.
Proceedings ArticleDOI
Chernoff-Hoeffding bounds for applications with limited independence
TL;DR: The limited independence result implies that a reduced amount and weaker sources of randomness are sufficient for randomized algorithms whose analyses use the CH bounds, e.g., the analysis of randomized algorithms for random sampling and oblivious packet routing.
Journal ArticleDOI
Randomized Distributed Edge Coloring via an Extension of the Chernoff--Hoeffding Bounds
TL;DR: Fast and simple randomized algorithms for edge coloring a graph in the synchronous distributed point-to-point model of computation and new techniques for proving upper bounds on the tail probabilities of certain random variables which are not stochastically independent are introduced.