The focus of Intelligent Systems laboratory at the University is on analysis of complex collective systems. The lab has adopted an interdisciplinary approach and is common place between computational social science initiative and collective intelligence. The primary agenda pursued by the lab include:

Our research in computational social science initiative is an interdisciplinary attempt to use computational formulations with broadly following two perspectives.

Constructing Generative Models: The focus in this perspective is to design agent based models and simulations for the social process or structure under study. This kind of generative modeling is different from the traditional approaches of equation based models and macro & micro-simulations. It is often visualized as a transition in social modeling from factors to actors. These models use a bottom-up approach and take the actors (role players in the concerned social setting) as the basic point of model. The actors are modeled as agents along with their attributes and their typical behaviours. Any rules governing agent interactions are also coded into the model and the model is then let to operate on its own. The system-level behaviour (and properties) is then observed and the outcome analyzed. What is interesting to see is how different system-level behaviours emerge out of local interactions between agents.

Social Media Analytics : This perspective focuses on designing and using computational formulations (those with capabilities for crawling, clustering, classification, topic discovery, entity extraction and sentiment analysis) to discover useful patterns in the posts on social web (such as on Blogs, Twitter and News sites). With the transformation of the World Wide Web into a more participative form, often referred to as Web 2.0, we now have billions of users who are not only consuming the data on the Web but are acting as co-creators. The social media analytics may be aimed at extracting inferences for commercial exploitation (such as finding the sentiment of bloggers on a product launch) or for a cross-cultural sociological analysis. It is the second aim which is more challenging. We strive to devise a computational setup that can analyze texts in all major languages in South Asia.

Our second major research agenda is design of collective intelligence based computational formulations for solving complex combinatorial optimization and real world search and optimization problems. We work on stochastic robust algorithmic formulations to solve problems with large and/ or complicated search space. A sample illustration of the work is designing a Particle Swarm Optimization or an Ant Colony Optimization algorithmic approach for clustering large amount of data.

We are trying to investigate collective systems of different kinds and at different cognitive levels, through an interdisciplinary approach. We hope that our lab will act as a major catalyst in setting up an interdisciplinary research centre on Intelligent Systems, as also envisaged in the University plans.