I am an ELBE postdoctoral fellow at CSBD, Dresden. I am interested in the physics of active matter, a family of systems in which individual constituents locally convert energy into mechanical motion at small scales. This enables them to flow or deform on long time scales without any external shear or force. Such self-driven movements play a crucial role in biological processes like morphogenesis and tissue organization. Studying active matter can, therefore, help us understand how biological shapes form and how they are encoded. The question of how such form-generating active processes are controlled is itself non-trivial, since biological control is inherently spatiotemporal and must remain robust despite the noise and disorder present in living systems.
More recently, I have developed an interest in the field of machine learning physics. The general goal of the field is to deduce algorithms that analyze experimental data and infer the physical rules ( such as partial differential equations) governing the observed dynamics. This is challenging because experimental data are often noisy, incomplete, and particularly in biological systems influenced by hidden variables that are not usually directly accessible. These difficulties make the problem both hard and exciting.
Before this, I did my PhD at Theory of Complex Systems Lab, under the supervision of Dr. Andrew Callan-Jones. Dr. Raphaël Voituriez was my co-supervisor. During my PhD, using a combination of analytical approaches and numerical techniques, I studied the behaviour of active fluids on curved surfaces. Prior to this, I did my master’s at TU Dresden in Computational Modelling and Simulation, where I worked with Dr. Steffen Rulands and Dr. Abhinav Sharma.