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*IMA Conference on The Mathematical Challenges of Big Data*

*16 – 17 December 2014                 Woburn House, London*

**

*Call for Abstracts***

The Big Data Revolution is one of the main science and technology
challenges of today. While this is multifaceted, mathematics is at the
very core of the challenge – in ranking information from vast networks
in web browsers such as Google, or identifying consumer preferences,
loyalty or even sentiment and making personalised recommendations, the
very scale of big data makes automation necessary and this, in turn,
necessarily relies on mathematical algorithms.  The challenge is to
derive value from signals buried in an avalanche of noise arising from
challenging data volume, flow and validity.  The mathematical challenges
are as varied as they are important. Whether searching for influential
nodes in huge networks, segmenting graphs into meaningful communities,
modelling uncertainties in health trends for individual patients,
linking data bases with different levels of granularity in space and
time, unbiased sampling, or connecting with infrastructure involving
sensors, privacy protection and high performance computing, answers to
these questions are the key to competitiveness and leadership in this field.

This event will highlight current challenges in mathematical methodology
alongside new mathematical problems arising from Big Data applications.

*//*

*Invited Speakers*

Mike Davies (University of Edinburgh)

David Hand OBE (Imperial College London)

Des Higham (University of Strathclyde)

Stephane Mallat (École Polytechnique, Paris)

Richard Norgate (Lloyds Banking Group)

Patrick Wolfe (University College London)

Panel to be chaired by Andrew Miller, MP

*//*

*Topics of interest*

Papers were invited to describe mathematical challenges specific to the
following topics or their application in large-scale use cases:

Optimal and dynamic sampling

Probably approximately correct methodologies

Uncertainty modelling & generalisation error bounds

Network analysis & community finding

Graph & web mining methods

Trend tracking & novelty detection

Stream data management

Dynamic segmentation & clustering

Transfer learning

Latent models for hierarchical data

Deep learning

Context awareness

Multimodal data linkage

Integration of multi-scale models

Mining of unstructured, spatio-temporal, streaming and multimedia data

Computational intelligence in large sensor networks

Predictive analytics and recommender systems

Real-time forecasting

Access on-demand in distributed databases

Affordable high performance computing

Privacy protecting data mining

Data integrity & provenance methods

Visualization methods

Mathematics underpinning large-scale use cases

*Call for Abstracts – Extended deadline:*

Papers will be accepted for the conference based on a 300-500 word
abstract for oral or poster presentation. We welcome abstracts to be
submitted by *Friday 14 November 2014* by e-mail to
[log in to unmask] <mailto:[log in to unmask]>. Please state
whether your title is intended for oral or poster presentation.

*Conference Fees:*

IMA Member £245

Non-IMA Member £340

IMA Student £150

Non-IMA Student £160

Conference Dinner at Hotel Russell on the evening of 16 December: £45

Please visit the conference webpage for details on registration:

http://ima.org.uk/conferences/conferences_calendar/big_data.html

*Programme Committee*

Paulo Lisboa, Liverpool John Moores University (Chair)

Peter Grindrod, University of Oxford

Giles Pavey, Dunnhumby

Richard Pinch, Cheltenham

Jennifer Scott, Science & Technology Facilities Council

Jared Tanner, University of Oxford

*Further information*

For further information on this conference, please visit the conference
webpage:

http://ima.org.uk/conferences/conferences_calendar/big_data.html

For general conference queries please contact Lizzi Lake, Conference Officer

E-mail: [log in to unmask]
<mailto:[log in to unmask]>              Tel: +44 (0) 1702 354 020

Institute of Mathematics and its Applications, Catherine Richards House,
16 Nelson Street, Southend-on-Sea, Essex, SS1 1EF, UK.


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-- 
Colin Johnson ([log in to unmask]),
Director of Graduate Studies, Faculty of Sciences,
Reader, School of Computing,
University of Kent, Canterbury, UK,
http://www.cs.kent.ac.uk/people/staff/cgj