We have constructed a world in which the potential for high-tech catastrophe is embedded in the fabric of day-to-day life.  -   Malcom Gladwell
EPSRC UKRI Innovation Fellowship - Multivariate Max-stable Processes with Application to the Forecasting of Multiple Hazards


July 2018

Over the next three years, this Fellowship aims to champion innovation in the statistical modelling of the structural components that capture how extremes from multiple phenomena are likely to manifest themselves jointly across a certain region and over time. Real life applications abound in the multivariate infinite-dimensional max-stable processes frameworks: the Fukushima nuclear disaster in 2011 was ignited by the combination of a huge earthquake followed by a tsunami; the sting jet phenomenon often unleashes very extreme local wind speeds, heavy rainfall and extreme temperatures on a nuclear plant; hurricanes like Harvey, Irma, Maria and Nate, which ravaged part of the US and Central America last year, are not so much surge events any longer, they are big but they are getting slower.


The research programme will have predominant focus on risk assessment aspects and will be carried out in the context of the Fellow’s ongoing collaborations with the Energy sector, in particular with EDF Energy. The statistical methodology to be developed as part of this EPSRC UKRI Innovation Fellowship will contribute to ensure safety standards and reliable operation of different stakeholders spanning the energy sector, which will ultimately give people access to more affordable energy, provide more interaction and safety and thus more choice.

October 2018

JOB in RESEARCH, Grade 6, full time running over 30 months: An opportunity has arisen for a post-doctoral research associate (PDRA) with an interest in statistical methods development to join the EPSRC funded research “Multivariate max-stable processes with application to the forecasting of multiple hazards”, based at the University of Reading, in collaboration EDF Energy R&D UK.

The successful applicant should have a PhD in Applied Mathematics, Statistics or a related discipline. Experience with methodological or complex applied projects would be desirable. The post holder will work directly with the EPSRC UKRI Fellow (Dr Claudia Neves, University of Reading) and with Dr Hugo Winter (EDF Energy) to undertake the research necessary towards meeting the aims of this grant. The direct contact of the post holder with EDF Energy, through placement time, will help to provide experience of working in an industry context and in presenting work to a wide range of end-users.


The University of Reading values diversity and is committed to equality of opportunity.

The School of Mathematical and Physical Sciences, comprising at that time the Department of Mathematics and Statistics and the Department of Meteorology, won the Silver Athena Swan award in 2010 and were delighted with a successful renewal of the Silver award in May 2014 and again in November 2017, in recognition of its good employment practices in relation to women working in science, technology, engineering, maths and medicine (STEMM).

FCT Exploratory Project DEXTE – Development of Extremes in Time and Space, EXPL/MAT-STA/0622/2013



Rainfall at nearby locations is strongly correlated. The mutual dependence can be quite different during heavy rainfall. The theory of max-stable processes offers a framework for modeling dependence at higher levels of the process. Using this framework questions about spatial rainfall, like extreme rainfall in a certain area, can be addressed. Max-stable processes are useful for – for example – problems on the amount of toxic elements in the air or water but also for problems about geological formations. The basic theory is well established (see [1]). We propose to develop the theory further in a useful way and to develop models for applying the theory. We will also develop an application of the theory to trends in rainfall in time and space (see output paper [2]).


[1] A. Ferreira and L. de Haan (2014). The Generalized Pareto process; with a view towards application and simulation. Bernoulli.

[2] L. de Haan, A. Klein Tank and C. Neves (2015). On tail trend detection: modeling relative risk. Extremes.