Research

My research integrates Artificial Intelligence, remote sensing, and geospatial analytics to architect scalable solutions for complex ecological and conservation challenges. I have pioneered the deployment of real-time computer vision frameworks to monitor globally threatened taxa — including swamp deer, critically endangered gharials, and turtles — leveraging cost-effective aerial platforms and deep learning architectures to bridge the gap between raw data and actionable conservation intelligence.

My current work advances into predictive spatiotemporal modeling, developing deep learning-based frameworks for large mammal movement prediction. Parallel to this, I am investigating LLM-driven Agent Frameworks and Retrieval-Augmented Generation (RAG) models to optimize the synthesis of multispectral sensor data for seamless conservation outputs.

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Peer Review — Journals