Date 16 – 17 December 2014
Location Woburn House, London
PROGRAMME
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, 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 should 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 case
Conference Fees
IMA Member £245
Non-IMA Member £340
IMA Student £150
Non-IMA Student £160
Conference fees include refreshments and lunches throughout the conference.
Conference Dinner at Hotel Russell on the evening of 16 December £45
To register for this event, please download, complete and return the application form.
Programme Committee
Paulo Lisboa (Liverpool John Moores University) – Chair; Peter Grindrod (University of Oxford); Giles Pavey (dunnhumby); Richard Pinch (GCHQ); Jennifer Scott (STFC Rutherford Appleton Laboratory); Jared Tanner (University of Oxford).
Further information
For general conference queries, or to register, please contact Lizzi Lake, Conference Officer
E-mail: conferences@ima.org.uk 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.
