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.

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.

Submissions are now closed.

Prof Sofia Olhede (Conference co-Chair, University College London)
Prof Patrick Wolfe (Conference co-Chair, University College London)
Prof Florentina Bunea (Cornell University)
Prof Petros Dellaportas (UCL)
Dr Arthur Gretton (UCL)
Dr Ioannis Kosmidis (UCL)
Dr Ioanna Manolopoulou (UCL)
Prof Richard Samworth (University of Cambridge)
Dr Ricardo Silva (UCL)
Prof Ming Yuan (University of Wisconsin-Madison)