When Semen samples are imaged under a microscope, they reveal how motile the sperm cells are in that sample (VISEM dataset). This 'motility' is directly related to the sperm's ability to travel the distance inside the female reproductive system to reach the egg and possibly fertilize it. Then a natural question arises: Can this motility be inferred from the microscale videos of semen samples? However, in a previous work from our group, we discovered that interaction between agents in a collective obfuscates the inference problem. This leads us to ask: Can the interactions between the sperm cells be inferred? We are also interested in finding out if these interactions lead to collective migration of these cells in large densities and numbers in models for the female reproductive system.

Whether it be the Madya Kailash junction in front of IIT where every human driver is trying to selfishly optimise their movement creating a traffic jam, or Guindy railway station during peak hours where crowded pedestrians are pushing each other as they board an upcoming train, Indian traffic offers some interesting peculiarities that makes these systems very interesting to study from a non-linear dynamics perspective. For instance, our traffic is heterogeneous in its composition – different kinds of vehicles or people with different purposes. Heterogeneity in turn affects overall movement and also makes it harder to automate safety protocols. We study the movement of Indian traffic (pedestrians or vehicular), how agents make decisions (using RL, MPC, etc.), and how the  differences in movement-heterogeneity affects several characteristics of crowd dynamics including safety.

It is often a non-intuitive task to predict or understand the motion of droplets through a network of microchannels. These devices have been found to show aperiodic patterns, large scale oscillations, synchronisation, etc. The complexity observed arises because a droplet changes the resistance for fluid motion in the branch it enters, which alters the flow in  entire network. This in turn affects the motion of these droplets and their decisions at a bifurcating channel giving rise to droplet motion through these channels that is nontrivial. In this work, we aim to develop computer-aided solutions to the design and operation of microfluidic networks where droplets have to be parked at designated areas.

Order parameters characterising the polarisation of the collective, in finite sized flocks, exhibit large scale fluctuations. This motivates one to ask: What underlying stochastic dynamical processes lead to the observed features? We have developed an easy to use, Python based package (PyDaddy) which extracts an 'interpretable' Stochastic Differential Equation Model for a given time series data. We use PyDaddy on time series generated from different flocking models to understand the nature of the mesoscale dynamics and its connections to the underlying microscale interactions. We plan to extend the method to identify SPDEs which capture not only the temporal variation but also the spatial variation of the order parameter in larger flocks.