Many modern industrial research and development problems involve a
mixture of deterministic and stochastic dynamics where the principal
challenge is the quantification and management of uncertainty at all
levels of the product development cycle. Problems dealing with
optimisation of stochastic processes, hierarchical modelling and
coarse-graining, uncertainty quantification, model selection and risk
assessment appear with increasing frequency at industry-academic
meetings such as the regular Study Groups with Industry organised by the
Smith Institute.

The classical framework of deterministic mathematical modelling in
which a problem is reduced to an ordinary or partial differential
equation to be solved by a mathematical scientist is inappropriate for
such problems due to the high level of noise. Purely data-driven
analyses based on the framework of classical statistics are equally
unhelpful since they often under-emphasise the generative mechanisms
underlying observed phenomena and lead to a proliferation of poorly
understood "black boxes".

A mathematical modelling framework in which stochastic processes play
a central role is essential to making progress in solving such
problems. This requires the ability to combine the insight and
understanding obtained from the mathematical analysis of deterministic
mathematical models with the power of modern statistical inference to
constrain the parameters and complexity of such models even in the
presence of high levels of uncertainty or noise.

The academic expertise to frame such problems already exists within
mathematics and statistics departments in the UK and at the University
of Warwick in particular. A new generation of mathematical scientists
are currently completing their PhDs in the likes of Warwick's Centre for
Complexity Science and MASDOC Centre for Doctoral Training where
mathematical modelling, analysis and statistical inference are taught
side by side.

This one-day meeting at the Isaac Newton Institute brought together
representatives from UK industry with early career researchers and PhD
students to establish relationships which will facilitate the transfer
of expertise in both directions across the academia-industry interface
in the mathematical sciences.

The Department for Communities and Local Government (DCLG) wanted to
engage with academic mathematicians and statisticians to address major
policy challenges and to inform its decision-making. The Turing Gateway
to Mathematics invited interested academics, and especially early career
researchers, to take part in this one-day workshop at the Isaac Newton
Institute in Cambridge on 26 March 2013.

Generally, DCLG works to move decision-making
power from central government to local councils. This helps put
communities in charge of planning, increases accountability and helps
citizens to see how their money is being spent.

Some of DCLG’s responsibilities include: