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The University of Southampton
The Alan Turing Institute

Turing Fellow awarded EPSRC New Horizons Funding

Published: 13 January 2021

EPSRC has announced three academics from the University of Southampton have been awarded a total of £600,000 from the New Horizons call; a programme aimed at high-risk discovery research focused on advancing knowledge and securing the pipeline of next-generation innovations.

Turing Fellow, Dr Alain ZemkohoProfessor Jonathan Essex and Dr Luca Sapienza were all successful in securing funds, in what was a unique funding call in terms of the type of research it was seeking and the process to apply.

New Horizons aims to help grow the portfolio of new, transformative research ideas in Mathematical Sciences and Physical Sciences research areas, feeding the future research landscape. Using a slimmed down and faster application process, funding has been awarded for completely new ideas which are essential to future-proof the research base, so it generates solutions to the as yet undefined problems confronting society.

Each of the academics have been awarded £200,000 for their projects which span 18-24 months.


The Projects

Dr Alain Zemkoho, Associate Professor within the School of Mathematics

Mathematical research that will primarily be applied to solve pessimistic bilevel optimization problems, which represent a powerful tool for modelling the interactions between various engineering, economic, and human systems. The results will aim to equip decision-makers with more realistic tools for well-informed decisions that will lead to innovative solutions for critical parts of the economy, such as transportation, machine learning technologies, healthcare and renewable energy industries.

For instance, for the large transportation projects currently planned or ongoing in the UK (e.g., Crossrail, HS2, and Heathrow 3rd Runway), bilevel optimization offers a framework for the government to maximize their outputs while ensuring that taxpayers are also able to achieve their expected objectives. Suitable bilevel optimization models in this context can be constructed around the optimal network design, establishing optimal toll policies where necessary or the optimal estimation of the demand for these facilities.

In addition, in the ongoing COVID-19 pandemic, social distancing has been identified as an important tool to combat the spread of the virus. Bilevel optimization can be used to develop tractable social distancing measures, where incentive policies are optimally built to modify the behaviour of people in public or private spaces of critical importance.

Jonathan Essex, Professor of Computational Systems Chemistry within Chemistry

Molecular simulations are an essential tool in the design of new drugs. In a molecular simulation, the atoms in the system move in response to the energy and forces acting on them, and by examining how the molecular arrangements change and respond we can develop and design new optimised molecules which feed into the drug design process. For example, by exploring how a drug binds to its receptor in the human body, known as its ‘binding geometry’, new interactions may be identified and exploited, leading to better drugs with higher affinity or better selectivity.

The extent to which current molecular simulations can explore the binding geometry is very limited. Conventional simulations are very efficient at sampling a particular binding geometry, but there are large energy barriers separating other possible binding geometries meaning that these are seldom observed in the simulations, if at all. There are a range of enhanced sampling algorithms, which seek to solve this problem of poor sampling. However, they all suffer from disadvantages that make them inefficient and requiring considerable system-specific optimisation.

Professor Essex’s proposal seeks to solve the sampling problem, by developing and applying a widely used sampling procedure from statistics - Sequential Monte Carlo. This approach will be general and adaptable. In doing so this high-risk and adventurous project will deliver robust new molecular simulation methodology to transform the discovery of new molecules and materials.

Dr Luca Sapienza, Associate Professor of Physics at the Department of Physics and Astronomy

Photosynthetic organisms rely on nano-scale molecular complexes to absorb sunlight and transfer the associated energy to initiate the chemical energy conversion steps that sustain life processes on Earth. Theoretical and experimental studies suggest that such light-harvesting complexes exploit molecular vibrations to optimise energy transfer processes. But how exactly such vibrations affect the efficiency, directionality and quantum properties of energy dynamics in photosynthetic units is yet to be fully understood.

To gain this understanding, this project aims at investigating, both theoretically and experimentally, the role of mechanical vibrations in the way bio-molecules transfer the energy that they can absorb from sunlight or, in the experiments, excitation laser sources.

By investigating bio-molecules embedded within nano-fabricated devices that can control mechanical vibrations, this research will shine new light onto the microscopic processes that control energy dynamics at the molecular scale. The knowledge created in this project will be the foundation to realise novel energy capture and transfer devices, by taking advantage of the ability to reverse engineering natural processes.

Notes for editors

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