I am a PhD student at the College of Information and Computer Sciences, UMass Amherst, where I am advised by David Jensen. My research aims to infer causal mechanisms that underlie the behavior of complex autonomous agents. This goal is driven by a strong desire to develop social understanding with AI agents as they continue to be integrated into society. To this end, my research interests span the areas of causal inference, probabilistic machine learning, and human-computer interaction. I am currently working on causal models for measuring competence in black-box reinforcement learning agents under interventions in the environment.

Before coming to UMass, I worked on clustering algorithms for large hypergraphs 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.