**4th IMA Conference on The Mathematical Challenges of Big Data**

The 4th Ima Conference on The Mathematical Challenges of Big Data is issuing a Call For Papers for both contributed talks and posters. Mathematical foundations of data science and its ongoing challenges are rapidly growing fields, encompassing areas such as: network science, machine learning, modelling, information theory, deep and reinforcement learning, applied probability and random matrix theory. Applying deeper mathematics to data is changing the way we understand the environment, health, technology, quantitative humanities, the natural sciences, and beyond ‐ with increasing roles in society and industry. This conference brings together researchers and practitioners to highlight key developments in the state‐of‐the art and find common ground where theory and practice meet, to shape future directions and maximize impact. We particularly welcome talks aimed to inform on recent developments in theory or methodology that may have applied consequences, as well as reports of diverse applications that have led to interesting successes or uncovered new challenges.

Contributed talks and posters are welcomed from the mathematically oriented data science community. Contributions will be selected based on brief abstracts and can be based on previously unpresented results, or recent material originally presented elsewhere. We encourage contributions from both established and early career researchers. Contributions will be assigned to talks or posters based on the authors request as well as the views of the organizing committee on the suitability of the results. The conference will be held in person with the option to attend remotely where needed.

**Inducement of sparsity, Heather Battey**

Sparsity, the existence of many zeros or near-zeros in some domain, is widely assumed throughout the high-dimensional literature and plays at least two roles depending on context. Parameter orthogonalisation (Cox and Reid, 1987) is presented as inducement of population-level sparsity. The latter is taken as a unifying theme for the talk, in which sparsity-inducing parameterisations or data transformations are sought. Three recent examples are framed in this light: sparse parameterisations of covariance models; systematic construction of factorisable transformations for the elimination of nuisance parameters; and inference in high-dimensional regression. The solution strategy for the problem of exact or approximate sparsity inducement appears to be context specific and may entail, for instance, solving one or more partial differential equation, or specifying a parameterised path through transformation or parameterisation space.

**Confirmed Invited Speakers**

Dr Heather Battey, Imperial College London

Prof. Lenka Zdebrova, EPFL (Swiss Federal Institute Technology)

Prof. Nando de Freitas, Google Deep Mind

Prof. Tiago de Paula Peixoto, Central European University

**Please follow the link below for Programme and Registration:**

https://ima.org.uk/17625/4th-ima-conference-on-the-mathematical-challen…