IMA Conference on the Mathematical Challenges of Big Data

Країна: Англія

Місто: London

Тези до: 03.10.2014

Дати: 16.12.14 — 17.12.14

Область наук: Фізико-математичні;

Е-мейл Оргкомітету:

Організатори: Liverpool John Moores University, University of Oxford


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.

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

Invited Speakers

Mike Davies (University of Edinburgh)
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

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