Statistical Science


First UCL Workshop on the Theory of Big Data, January 2015

30 May 2014

Big Data has become ubiquitous in modern society. It challenges state-of-the-art data acquisition, computation and analysis methods. Much focus has been placed on the application of Big Data methods, less of a focus on the theoretical underpinning of the field.

The aim of this workshop is to gather together experts in Big Data methodology, e.g. the creation and understanding of Big Data analysis tools, to understand how computational bottlenecks trade-off with statistical efficiency. In addition the workshop will study such trade-offs and additional analysis problems arising when analysing vast representations of association, such as when using network models. 

The Departmental of Statistical Science of University College London welcomes contributions to the First UCL Workshop on the Theory of Big Data, to take place 7-9 Jan 2015 at University College London Antatomy J Z Young LT and Gavin de Beer LT.

Scope: contributions within the broad theme of theoretical, computational and statistical underpinnings of Big Data analysis, emphasizing challenges and opportunities that are not usually found in traditional data analysis problems. We particularly invite submissions related to the following two focus areas:

  • measures of association such as covariance modelling, and networks;
  • statistical optimality versus computational efficiency.

List of speakers include: Anima Anandkumar (UCI), Jianqing Fan (Princeton), Chris Holmes (Oxford), Chenlei Leng (Warwick), Nicolai Meinshausen (ETH), Peter Orbanz (Columbia), Gesine Reinert (Oxford), Peter Richtarik (Edinburgh), Philippe Rigollet (Princeton), Rajen Shah (Cambridge), Patrick Wolfe (UCL), Qiwei Yao (LSE).

Modus: Accepted contributions will be presented as either 20 minutes talks or poster presentations. 

Submission process: submissions should happen not later than 6th of September. The submission takes the form of an extended abstract (up to 1 A4) describing the potential contribution, which can be novel or related to an existing pre-print or publication.

Submissions should be made via the link below.

Communication of acceptance: 30 September 2014.