BigData 2017 - 3rd UCL Workshop on the Theory of Big Data

3rd UCL Workshop on the Theory of Big Data ? Call for Papers:
The Department of Statistical Science, University College London welcomes contributions to the 3rd UCL Workshop on the Theory of Big Data, which will take place 26th ? 28th June 2017 at University College London.
Abstract submission process and deadlines:
Submissions should not happen later than 14th March 2017. The submission takes the form of an extended abstract (max 1 page A4), describing the potential contribution, which can be novel or related to an existing pre-print or publication. Submissions should be made via email, to big-data-AT-ucl.ac.uk.
Modus: Accepted contributions will be presented as either 15 minute talks, or as poster presentations.
Communication of decisions: 24th March 2017.
Workshop description: Big Data has become ubiquitous in modern society, but drawing insights from it remains a challenge due to its unprecedented degrees of heterogeneity, often compounded by inadequate experimental design. The past decade has seen considerable developments with big data algorithms, but significant challenges remain for the area’s theoretical underpinning.
The aim of this workshop is to gather experts who develop theory and methodology for big data sets; i.e. scientists who construct new algorithms, but also develop theoretical understanding as to the analysis techniques that are optimal or preferable in different sampling scenarios. The workshop will feature research into computational and statistical efficiency trade-offs, high-dimensional dependency structures (such as spatiotemporal models), as well as high-dimensional estimation and learning, and privacy-preserving algorithms.
Scope of the workshop: Contributions within the broad theme of theoretical, computational and statistical underpinnings of Big Data analysis, emphasising challenges and opportunities that are not usually found in traditional data analysis problems. We particularly invite submissions related to the following three focus areas:
? Challenges in Spatial & Temporal Analysis
? High-Dimensional Estimation and Learning
? Privacy-preserving inference
? Tensors and statistical modelling
Confirmed conference speakers include:
Heather Battey (Imperial College London)
Kamalika Chaudhuri (University of California, San Diego)
Arnak Dalalayan (ENSAE ParisTech)
Peter Diggle (University of Lancaster)
Jianqing Fan (Princeton University)
Arthur Gretton (UCL)
Peter Hoff (Duke University)
Lieven Lauthauwer (Université catholique de Louvain)
Guy Nason (Bristol University)
Marten Wegkamp (Cornell University)
Ming Yuan (Wisconsin-Madison)
Tian Zhang (Columbia University)