Pushkarini Agharkar

Papers

Total Citations

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About

Pushkarini Agharkar is a researcher specializing in robotic surveillance, stochastic optimization, and Markov chain theory, with a particular focus on autonomous systems for anomaly detection in networked environments. Her most influential work, "Robotic Surveillance and Markov Chains With Minimal Weighted Kemeny Constant" (2015, 62 citations), represents a significant theoretical advancement in the field, introducing a generalized framework for optimizing mean first passage times — known as the Kemeny constant — to account for heterogeneous travel and service times in real-world robotic patrol scenarios. This work builds upon her earlier 2014 contribution (14 citations), where she first proposed stochastic surveillance strategies grounded in Markov chain optimization for quickest anomaly detection across discrete network environments. Expanding her research further, her 2016 paper "Quickest Detection Over Robotic Roadmaps" (9 citations) tackles the challenging problem of anomaly detection under extreme sensor uncertainty, applying the Ensemble CUSUM Algorithm to arbitrary graph topologies. Together, these contributions establish Agharkar as a thoughtful contributor to the intersection of robotics, probabilistic modeling, and sequential detection theory, offering both rigorous mathematical foundations and practical implications for autonomous surveillance system design.