Uttar Pradesh, India
Passionate about building intelligent technologies that solve real-world problems through AI, Machine Learning, Software Engineering, and Wireless Communications. My work spans healthcare AI, computer vision, and next-generation communication systems through international research collaborations and multidisciplinary projects. Beyond research, I enjoy leading teams, transforming ideas into impactful solutions, and learning from every challenge. I believe that innovation is driven by curiosity, collaboration, and a commitment to continuous growth. "The final product of development is not a product - it's the person you become in the process."
Working on advanced Vehicular Visible Light Communication (V2V-VLC) systems under realistic wireless channel conditions. Studying Signal-to-Interference-plus-Noise Ratio (SINR) distribution and ergodic capacity analysis for MIMO-based VLC systems under atmospheric turbulence. Contributing to channel modeling incorporating path loss, mobility, interference, and environmental effects. Assisting in analytical modeling, simulation, and performance evaluation for next-generation optical wireless communication systems.
Leading a team of research interns at TU Bergakademie Freiberg (founded in 1765, one of the world’s oldest mining and metallurgy universities) under Dr. Sven Groppe, working on integrating Data Structures & Algorithms with Machine Learning, contributing to academic research and collaborative writing.
Focused on AI-driven biomedical signal interpretation for neurological and balance disorder analysis.
Selected to begin a research role focused on developing resource-efficient AI frameworks for healthcare applications in Edge–Fog–Cloud environments. Work involves dual-feature extraction, system optimization, and scalable deployment strategies.
Worked on a systematic literature review, prepared a food image dataset, and developed an AI/ML-based mobile application for assessing food freshness. Also authored a review paper titled “Image-Based Food Quality and Freshness Assessment Using Machine Learning and Mobile Applications: A Comprehensive Review.”