Collective Phenomena Lab,
Chemical Engineering, IISc Bangalore, India
Looking for a suitable candidate with a Bachelors or Masters degree in any Engineering discipline, Physics or Mathematics preferably with a penchant for studying non-linear dynamical systems and comfortable in writing code (Python, MATLAB, etc.).
Previous experience with active matter, agent based modeling, data-science research are most welcome!
If you are interested, please write to me stating why you are interested in the project, with your CV attached.
Inferring characteristics from data
Agent based models for crowd motion;
Real world collectives are heterogeneous; each agent in the collective is different from the other in some measure. In a pedestrian crowd, you may have people who want to slow down or change direction from the rest of the crowd; in cell-migration studies you find leader-follower cells; in synthetic active matter systems the observed dynamics is subject to the heterogeneity in the assembled Janus particles. Can this heterogeneity be uncovered from data?
The objective of this project would be to derive techniques to infer properties intrinsic to agents from their movement data. One could take three routes: (1) use the time-averaged information from both the focal agent and its neighbours, (2) use the dynamic information on how the focal agent slowed down or sped-up, or (3) a combination of the two, to uncover the intrinsic property of the focal agent.
Initial work in this area: Observing a group to infer individual characteristics
Modelling intelligent collectives
Causal entropy maximisation;
Agents with memory;
Agents that learn;
Often while studying collective motion of living organisms, agent behaviour is modelled using simple rules; examples include how agents align or attract for the group to stay together and travel in the same direction. However, these rules are context specific manifestations of a more complex cognitive decision making. This raises a question: how does one model agents that think?
In this project, we will first take models for cognition from literature -- maximising causal entropy or future-state maximisation principle -- and study the collectives where agents move based on these principles. We will design several specific contexts for the collectives and explore the different collective phenomena that emerge.
Intrinsically motivated collective motion
Structural transition in the collective behavior of cognitive agents