I am a researcher in the group of Giovanni Volpe in the Department of Physics at the University of Gothenburg in Sweden.

My research lies at the intersection of theoretical physics and machine learning, combining mathematical modelling techniques with numerical simulation to tackle novel problems in AI and the biological sciences.

My current work focuses on decentralised deep learning, specifically the development of novel machine learning techniques used to self-organise and self-optimise a swarm of autonomous robots, which has subsequently been realised in a real-world robot swarm.

I have worked in theoretical soft matter physics, particularly the computational modelling of non-equilibrium biological systems across all scales.

Previously, I was a postdoctoral researcher in the group of Joakim Stenhammar in the Division of Physical Chemistry at Lund University in Sweden. I worked on active turbulence in suspensions of microswimmers, such as the bacteria E. Coli: active matter at the microscale. This used statistical modelling and numerical simulation via HPC to investigate and characterise its non-linear chemotactic behaviour.

I was awarded my PhD in 2019, completed under the supervision of Matthew Turner, studying the dynamics of bird flocks, specifically under topological constraints: active matter at the macroscale. (Read my thesis here: “Topological models of swarming”)

 

(Left) Bird flocking: Sort sol at Ørnsø.  (Right) Active nematic: confined microtubules.