Project 1: Information spreading in complex networks (With Subrata, and Abhishek)

Deterministic and stochastic (Markov chain Monte Carlo-based simulation) approach. application to real data e.g COVID-19 cases, Governmental awareness programs through social networks.  Epileptic seizure in brain networks.

Target:  Vector –Borne diseases, contact networks, and mapping of the data in geospatial locations

Project 2: Design principles of TRNs  (With Satyaki and Prosenjit)

Transcriptional regulatory networks (TRNs) are biological networks consisting of proteins, called transcription factors (TFs), and genes. They capture how the genes are regulated by the TFs and regulate protein synthesis in living cells. TRNs are built out of subnetworks, called motifs, which have several functional properties in information spread dynamics. In this project, we are employing network science and machine learning to understand the nature of connectivity among the motifs to achieve better characterization of the design principles of TRNs and information flow dynamics.

 

Project 3: Mixing pattern extraction from infection spreading (With Satyaki, Subrata, and Prosenjit)

COVID-19 has had a massive impact on human life. We are trying to carry out computational research that identifies factors affecting infection spread and propose recommendation measures to curb them. It has been shown that higher social interaction or mixing will lead to increased infection spread. As part of our prior work, we have proposed latent factors (LFs) that will quantify this social mixing. In this project, we will attempt to connect the LFs to real-world socioeconomic and demographic factors to better understand ways to minimize the spread of infection by designing effective lockdown and behavioral changes.