We are please to report that Dr Maria Liakata has received a Turing Artificial Intelligence (AI) Fellowship.

The Fellowships from The Alan Turing Institute, the UK’s national institute for data science and AI, aim to attract and retain exceptional researchers in artificial intelligence. Covering a broad view of AI, including applications of foundational disciplines across mathematical sciences, statistical sciences, computational sciences and engineering, Fellows collaborate across disciplines and have the opportunity to collaborate with academia, industry, government and the third sector.

Dr Liakata’s Fellowship will focus on creating time sensitive sensors from language and heterogeneous user generated content. Commenting on the research she said:

“Wide spread use of digital technology has made it possible to obtain language data (e.g., social media, SMS) as well as heterogeneous data (e.g., mobile phone use, sensors) from users over time. Such data can provide useful behavioural cues both at the level of the individual and the wider population, enabling the creation of longitudinal digital phenotypes.

“Current methods in natural language processing (NLP) are not well suited to time sensitive, sparse and missing data, collected over time or personalised models of language use. The Turing AI fellowship will allow me to establish a new area in NLP on personalised longitudinal language processing.

“I plan to develop sensors for capturing digital biomarkers from language and heterogeneous user generated content to understand the evolution of an individual over time. I want to make a significant contribution to mental health by working with clinical experts to create new tools based on the sensors, making it possible to assess and measure conditions in between clinician appointments.”

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The Statistics Department has recently been awarded funding from NERC for the project "Statistical inference and uncertainty quantification for complex process-based models using multiple data sets". Principal Investigator Richard Everitt, will collaborate on the project with other members of the Department (Rito Dutta, Christian Robert and Martyn Plummer), the Ecology group at the University of Reading, and with the Centre for Environment, Fisheries and Aquaculture Science.

Making responsible decisions about landscapes is facilitated by the use of complex models able to represent multiple competing demands on land use. Decisions about land use require that trade-offs between competing demands be identified, and their consequences through time be characterised. Models consisting of stochastic computer simulations are increasingly used to make realistic predictions about real world processes from socio-ecological systems involving land use. dels attempt to simulate all relevant aspects of a real physical system, they may involve many parameters, some of which will be difficult to set correctly. The final objective of these models is to assess the possible consequences of management decisions, such as the placement of wind turbines, thus it is crucially important that the uncertainty introduced by calibrating parameter values be understood.

In order to make informed decisions, one needs to be able to consider the effects of a number of complex interacting temporal and spatial processes (e.g. hydrological, ecological, agricultural, economic, climate). The project will develop new techniques in Approximate Bayesian Computation to enable parameter estimation for models for these processes, taking into account the impact of model misspecification. This project is part of the Strategic Priorities Fund on Landscape Decisions. https://landscapedecisions.org/

A Higgs boson matching that predicted in the Standard Model was found in 2012. However, many theories such as string theory, which attempts to unite quantum mechanics and gravity, tells us there should be at least four more. ATLAS has just published a search for a second Higgs boson, with a mass between 2 and 20 times that of the first, decaying to pairs of tau leptons. In many models this search is the most sensitive yet - but still no evidence for another Higgs boson is found.

Antibiotic resistance is an increasing battle for scientists to overcome, as more antimicrobials are urgently needed to treat biofilm-associated infections. However, Dr Freya Harrison and colleagues say research into natural antimicrobials could provide candidates to fill the antibiotic discovery gap.

Press Release (28 July 2020)

Warwick Engineering researchers have been inspired by the unique movement of trembling aspen leaves, to devise an energy harvesting mechanism that could power weather sensors in hostile environments and could even be a back-up energy supply that could save and extend the life of future Mars rovers

The Warwick Mathematics Institute is saddened to hear of the passing of Prof. Lawrence Markus in January 2020.