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Serving at NSF panels and what it teaches about how to pitch the perfect proposal

NSF is one of the largest funding sources for academic research.  It accounts for about one-fourth of federal support to academic institutions for basic research. NSF accepts 1000s of proposals from researchers, and organizes peer-review panels to decide which ones to fund.

Serving at NSF panels are fun. They are also very useful to understand the proposal review dynamics. NSF funding rates are around 10% for computer science and engineering research proposals, so understanding the dynamics of the panel is useful for applying NSF to secure some funding.

How do you get invited as a panelist?  You get invited to serve at an NSF panel by the program director of that panel. (Program directors are researchers generally recruited from the academia to serve at NSF for a couple years to run panels and help make funding decisions.)

If you have been around and have successfully secured NSF funding, you will get panel invitations. They will have your name and contact you. But, if you are new, do…

paper summary: One Trillion Edges, Graph Processing at Facebook-Scale

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This paper was recently presented at VLDB15.
A. Ching, S. Edunov, M. Kabiljo, D. Logothetis, S. Muthukrishnan, "One Trillion Edges: Graph Processing at Facebook-Scale." Proceedings of the VLDB Endowment 8.12 (2015).

This paper is about graph processing. Graphs provide a general flexible abstraction to model relations between entities, and find a lot of demand in the field of big data analysis (e.g., social networks, web-page linking, coauthorship relations, etc.)
You think the graphs in Table 1 are big, but Frank's laptop begs to differ. These graphs also fail to impress Facebook. In Facebook, they work with graphs of trillion edges, 3 orders magnitude larger than these. How would Frank's laptop fare for this? @franks_laptop may step up to answer that question soon. This paper presents how Facebook deals with these huge graphs of one trillion edges.

Apache Giraph and Facebook In order to analyze social network data more efficiently, Facebook considered some graph proc…

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