My research trajectory has been driven by fundamental questions in non-equilibrium soft matter physics: "How do non-equilibrium processes, such as crack nucleation and polymer instabilities, dictate local structure and emergent properties in soft materials, and how can these dynamics be quantitatively modelled and controlled?"
By combining physical modelling, non-linear partial differential equations (PDEs), data-driven machine learning (ML), and experimental optics, my goal is to uncover universal underlying patterns and leverage these insights to build predictive frameworks that add functional value to soft material systems.
Theoretical & Computational Modelling: Non-linear PDEs, continuum mechanics, phase-field models, and numerical simulations of non-equilibrium dynamics.
Data-Driven Discovery & ML: Machine learning models for pattern recognition, automated feature extraction, phase classification, and predictive modelling of structure–property relationships.
Experimental Characterisation: Advanced optical instrumentation, quantitative image processing, and custom microfluidic platforms.
Research interest keywords:
Soft Matter Physics | PDEs & Continuum Modelling | non equillibrium Statistical Physics