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Key Areas of Research

Complexity, Emergence & Upscaling

Specific research:

  • general methodologies for variable reduction on networks

  • the identification of continuum limits in particle systems

  • coherent phenomena in turbulent systems.

Applications:

  • complex systems theory
  • weather & climate
  • controlling stability of fusion plasmas.

Complex Fluids and Complex flows

Specific research:

  • Granular and foam systems - explicit particle based simulations of the flow properties
  • Soft (Brownian) systems - wavelet-based techniques to incorporate long range hydrodynamic coupling.
  • Simulating flow through particular geometries using up-scaled pseudo particles (versions of Dissipative Particle Dynamics) to capture viscoelastic and intertial effects.

Applications:

  • flow of people, cars
  • granular materials
  • diagnosis of cancer, hypertension, heart disease

Clustering, Condensation and Jamming

 

Specific research:

  • how fast do clusters grow, how large, and even effectively infinite?
  • localised condensate (e.g. traffic jam) vs background (e.g. flowing traffic) at critical density:
  • connecting biology (molecular transport, ant trails), social sciences (traffic and transport modeling) and physics (granular media, Bose Einstein condensation).

Applications:

  • weather & climate
  • flow of people, cars
  • granular materials

Complex Networks & their dynamics

Specific research:

  • neuroscience - how neuronal details such as spatial extension and the propagation of calcium waves influence overall neural computation.
  • markets - how local bartering rules can lead to the emergence of market prices with realistic stochastic dynamics.
  • epidemiological research - how contact networks propagating infection interact with the social networks propagating adoption of preventative measures.
  • biodiversity- how the pattern of clone boundaries conforms to classical growth interface models
  • early genetic evolution and speciation - role of fitness and horizontal gene transfer.

Applications:

  • infectious diseases
  • neural computing
  • data storage
  • dynamics of opinions & markets

Network Statistical Inference

Specific research:

  • multiple fields, from molecular biology to health and economics.
  • novel methods for network learning, including Bayesian approaches, MCMC and penalized likelihood methods.
  • in cancer protein signalling networks - breaking new ground in the field and have implications for other diseases as well as signalling biology more generally.
  • plant pathology - specifically the response of the model organism
  • public health - relationships, as captured in a social network, can influence health status (over and above the contribution of other factors):
  • we are developing statistical network models to analyse a suitable database on adolescent health.
  • social economics - advanced inference approaches to probe the relationship between subjective well-being and risk-taking.

Applications:

  • diagnosis of cancer, hypertension, heart disease
  • data storage

New applications of statistical mechanics

Specific research:

  • well developed set of tools for fresh applications in molecular biology, traffic theory, opinion dynamics

Applications:

  • granular materials
  • dynamics of opinions and markets
  • molecular biology