Manisha Khanduja

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About

Manisha Khanduja is a researcher whose work centers on the transformative potential of Edge Artificial Intelligence, particularly its deployment in real-world systems. Her most-cited paper, "Recent Challenges on Edge AI with Its Application: A Brief Introduction" (2022), has garnered 4 citations, establishing a foundational overview of the field's critical hurdles—such as latency, bandwidth constraints, and data privacy—while mapping practical applications from smart healthcare to industrial IoT. Khanduja’s contribution lies in synthesizing these challenges into a clear framework, helping both practitioners and academics navigate the transition from cloud-centric AI to decentralized, on-device intelligence. By highlighting the trade-offs between computational efficiency and model accuracy, she has provided a roadmap for optimizing AI in resource-limited environments. Her work is particularly valuable for students and engineers seeking to understand the bottlenecks of real-time inference at the edge. Khanduja’s research not only identifies pressing obstacles but also inspires future innovations in low-power, privacy-preserving AI systems, marking her as a thoughtful voice in the evolution of intelligent, distributed computing.