Focusing on Applied AI, Machine Intelligence, predictive analysis, and Data Science in Eco-informatics. Utilizing advanced computer vision and machine learning for environmental research within the Department of Information Science and Technology.

Interdisciplinary research at the intersection of AI, UAVs, and remote sensing. Developed real-time data pipelines and Earth system modeling applications. Proficient in LLMs, RAG methods, and multi-agent architectures for biodiversity monitoring.

Doctoral research on AI-driven ecological monitoring — UAV-based wildlife detection, riparian habitat assessment, and spatiotemporal analysis of the Ganga River ecosystem.

Full-stack development using ASP.NET, C#.NET, Web Services (SOAP/XML), and database management for mission-critical internal applications.

End-to-end website design, troubleshooting, and 24/7 maintenance of production projects.

Internship training on .NET development projects.

Thesis: AI-driven multi-sensor ecological monitoring and spatiotemporal habitat analysis using UAVs and deep learning.

Master of Science in Geo-Informatics