Priyesh V | Robert Bosch Center for Data Science and Artificial Intelligence

MS Scholar

Representation learning

Semi-supervised learning

My research primarily evolves around learning efficient representations for entities in irregular domains. I use complex networks theory to design efficient (deep) machine learning models that can represent and make inferences on complex structured data.

Sep 5, 2020

Finding Influencers in Social Networks: Reinforcement Learning Shows the Way

B Ravindran , Harshavardhan P V , Priyesh V , Bryan Wilder , Milind Tambe

Social Network Analysis has given us many tools to effectively manage information dissemination in a social group, study growth and dynamics in such groups, etc. But one of the key challenges when studying social groups of underprivileged or socially marginalized groups is the recovery of the underlying social network itself. This study proposes a machine learning approach for learning to effectively allocate a limited budget to discover the network.

AI , Reinforcement Learning , Social Network , Influence Maximation , Network Graph

5 min read