Srikanth Thudumu

German Research Centre for Artificial Intelligence, Deakin University

Papers

Total Citations

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About

Srikanth Thudumu is a researcher whose work bridges cutting-edge artificial intelligence with practical, real-world applications. His primary research areas include active object tracking, multi-agent systems, and federated learning, with a particular focus on enhancing autonomous navigation, surveillance, and robotics. Thudumu’s major contribution lies in advancing cooperative multi-agent systems for active object tracking, as demonstrated in his most-cited paper, "CSAOT: Cooperative Multi-Agent System for Active Object Tracking" (2025, 2 citations). This work addresses a critical challenge in computer vision by enabling multiple agents to collaboratively track objects in dynamic environments, surpassing the limitations of passive tracking methods. Additionally, his research on "An Integrated Federated Learning and Meta-Learning Approach for Mining Operations" (2023, 1 citation) showcases his ability to apply sophisticated machine learning techniques to industrial contexts, improving data privacy and model adaptability in resource-constrained settings. While his citation counts are modest, Thudumu’s work is notable for its innovative integration of multi-agent coordination and learning paradigms, offering promising pathways for more responsive and intelligent tracking systems. His contributions are particularly relevant for researchers exploring autonomous systems and distributed AI, positioning him as an emerging voice in these fields.