CoPhe LAB

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New papers!
Randomness in choice leads to cohesion in moving groups
Disentangling intrinsic and collective effects from data to perform better 'learning'

Welcome to the Collective Phenomena Lab, (CoPhe LAB). Our research interests broadly encompass understanding the nonlinear interactions and collective dynamics in natural and artificial systems. We look at problems across a wide spectrum of systems that include, droplets in a microchannel, collective organismal motion, traffic and pedestrian dynamics. We use a combination of simple agent based models, data-driven approaches and machine learning to solve two kinds of problems:

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1. The forward problem:

   • Understanding how a given set of interactions between agents lead to interesting and useful features at the level of the collective.

2. The inverse problem: 

   • Designing or tuning the inter-agent interactions and the surroundings to achieve the desired group-level behavior [the engineering problem] or

   • Inferring the interactions between agents from data of the collective [data-science problem].