I am a second-year MS student at the College of Information and Computer Sciences, UMass Amherst, where I am advised by David Jensen. I am interested in using causal inference methods to build machine learning models that generalize to novel environments and afford explainability. My current research focuses on learning representations for causal modeling that exhibit modularity and independence of mechanism.

Before coming to UMass, I worked at the Indian Institute of Technology Madras, where I was advised by Prof. Balaraman Ravindran. I graduated with a B.E in Electrical and Electronics Engineering from SSN College of Engineering, affliated to Anna University.


A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering
Tarun Kumar*, Sankaran Vaidyanathan*, Harini Ananthapadmanabhan, Srinivasan Parthasarathy, Balaraman Ravindran
8th International Conference on Complex Networks and their Applications (Complex Networks 2019)

Abstract | arXiv

Clustering on hypergraphs has been garnering increased attention with potential applications in network analysis, VLSI design and computer vision, among others. In this work, we generalize the framework of modularity maximization for clustering on hypergraphs. To this end, we introduce a hypergraph null model, analogous to the configuration model on undirected graphs, and a node-degree preserving reduction to work with this model. This is used to define a modularity function that can be maximized using the popular and fast Louvain algorithm. We additionally propose a refinement over this clustering, by reweighting cut hyperedges in an iterative fashion. The efficacy and efficiency of our methods are demonstrated on several real-world datasets.