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Career
- 2022-: Professor of Statistical Mechanics (DAMTP / Chemistry), University of Cambridge
- 2018-: Fellow, Emmanuel College
- 2017-2022: Interdisciplinary Lecturer (DAMTP / Chemistry), University of Cambridge
- 2008-2017: Lecturer / Reader, University of Bath
- 2006-2008: Postdoc, College of Chemistry, UC Berkeley
- 2004-2006: Postdoc, Theoretical Physics, Oxford University
- 2000-2004: PhD, Physics, Imperial College London
Research
Statistical Mechanics of Soft Matter, combining ideas and methods from mathematics, physics and chemistry.
Publications
Role of initial conditions in $1D$ diffusive systems: compressibility,
hyperuniformity and long-term memory
(2022)
Direct imaging of contacts and forces in colloidal gels
– The Journal of Chemical Physics
(2022)
156,
214907
(DOI: 10.1063/5.0089276)
Rectification in a mixture of active and passive particles subject to a ratchet potential
– Journal of Statistical Mechanics: Theory and Experiment
(2022)
2022,
043203
(DOI: 10.1088/1742-5468/ac601f)
Tracer dynamics in one dimensional gases of active or passive particles
– Journal of Statistical Mechanics: Theory and Experiment
(2022)
2022,
013209
(DOI: 10.1088/1742-5468/ac4801)
Cycle counts and affinities in stochastic models of nonequilibrium systems.
– Phys Rev E
(2021)
104,
064137
(DOI: 10.1103/PhysRevE.104.064137)
Bayesian inference across multiple models suggests a strong increase in lethality of COVID-19 in late 2020 in the UK
– PloS one
(2021)
16,
e0258968
(DOI: 10.1371/journal.pone.0258968)
Irreversibility and Biased Ensembles in Active Matter: Insights from Stochastic Thermodynamics
– Annual Review of Condensed Matter Physics
(2021)
13,
215
Dynamical phase transition in the activity-biased fully-connected random field Ising model: connection with glass-forming systems
– Journal of Statistical Mechanics Theory and Experiment
(2021)
2021,
103206
(DOI: 10.1088/1742-5468/ac2895)
Critical point for demixing of binary hard spheres.
– Physical review. E
(2021)
104,
044603
(DOI: 10.1103/PhysRevE.104.044603)
Efficient Bayesian inference of fully stochastic epidemiological models with applications to COVID-19.
– Royal Society open science
(2021)
8,
211065
(DOI: 10.1098/rsos.211065)
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