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Small Summaries for Big Data

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  • Full or part time
    Dr Cormode
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

A funded studentship is available in the area of computer science to work on algorithms and data structures for summarization of massive data sets. Funding is provided through the prestigious ERC program, under project 647557, “Small Summaries for Big Data”.

Project Overview:
A fundamental challenge in processing the massive quantities of information generated by modern applications is in extracting suitable representations of the data that can be stored, manipulated and interrogated on a single machine. A promising approach is in the design and analysis of compact summaries: data structures which capture key features of the data, and which can be created effectively over distributed data sets. Popular summary structures include the Bloom filter, which compactly represents a set of items, and sketches which allow vector norms and products to be estimated.
Such structures are very attractive, since they can be computed in parallel and combined to yield a single, compact summary of the data. Yet the full potential of summaries is far from being fully realized. Professor Cormode is recruiting a team to work on important problems around creating Small Summaries for Big Data. The goal is to substantially advance the state of the art in data summarization, to the point where accurate and effective summaries are available for a wide array of problems, and can be used seamlessly in applications that process big data. PhD studentships can work on a variety of topics related to the project, including:
• The design and evaluation of new summaries for fundamental computations such as large matrix computations
• Summary techniques for complex structures such as massive graphs
• Summaries that allow the verification of outsourced computation over big data.
• Application of summaries in the context of monitoring distributed, evolving streams of data
The expectation is that this will lead to novel results in the summarization of large volumes of data, which will be published in top-rated venues.
You will possess a degree in Computer Science, mathematics or very closely related discipline (or you will shortly be obtaining it). You should have good knowledge of one or more of the following areas: algorithm design and analysis; randomized and approximation algorithms; communication complexity and lower bounds; streaming or sublinear algorithms. The post is based in the Department of Computer Science at the University of Warwick, but collaborations with closely related research organizations such as the centre for Discrete Mathematics and its Applications (DIMAP), the Warwick Institute for the Science of Cities (WISC); and the newly formed Alan Turing Institute (ATI) will be strongly encouraged.
For examples of relevant research and related topics, please consult Prof. Cormode’s web pages at http://www2.warwick.ac.uk/fac/sci/dcs/people/Graham_Cormode

Eligibility:
Candidates should hold a degree in Computer Science, Mathematics or closely related discipline, or expect to complete one before the commencement of the studentship. The degree should show a high level of achievement (1st or 2.1 level).
Funding level:
Funding is available to support stipend and fees at the UK/EU level for 4 years (this does not cover fees for non-EU students, see http://www2.warwick.ac.uk/study/postgraduate/funding/fees/ for more information).
Application details:
Please send a CV to [email protected] giving details of your education and achievements to date, including details of performance in relevant university-level subjects (such as Algorithms, Data Structures, Complexity, Mathematical analysis of algorithms, linear algebra and so on). Please also include a covering note explaining how your background and interests make you relevant to the aims of the project.
Applications will be reviewed as they are received, with an initial deadline of November 30th 2015.

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 647557).

Funding Notes

Funding is available to support stipend and fees at the UK/EU level for 4 years (this does not cover fees for non-EU students, see http://www2.warwick.ac.uk/study/postgraduate/funding/fees/ for more information).

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