I received my Ph.D. in Electronics and Communication Engineering from the National Institute of Technology Silchar, India (2022). My dissertation, Pattern Recognition Based on Microwave Signal Using Artificial Intelligence, focused on pattern recognition using antenna reactive fields, with applications in human activity recognition (HAR) and machine health monitoring.

Following my Ph.D., I joined the Madhav Lab at the Indian Institute of Technology Kanpur as a Post-Doctoral Fellow, where I worked at the intersection of machine learning and audio signal processing. Drawing on my training as a Western classical guitarist, my research naturally shifted towards music information retrieval (MIR). At IIT Kanpur, I developed and deployed an audio-based recommendation system for India’s national broadcaster, Prasar Bharati, leading the integration of AI models for large-scale multimedia search and recommendation.

I am currently a Post-Doctoral Fellow at the RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Norway. My present research focuses on rhythmic analysis of music and dance, with an emphasis on multimodal synchronization. Specifically, I am working on learning-based methods for estimating dance tempo directly from motion-capture data, developing adaptive algorithms that integrate rhythmic information across multiple body segments to achieve state-of-the-art accuracy across diverse dance genres.

Research Interest:

My research lies at the intersection of machine learning, audio signal processing, and music information retrieval, with a special focus on rhythmic analysis of music and dance. I develop learning-based methodologies for audio and movement analysis, exploring multimodal synchronization between music and the body. Currently, I am investigating approaches that integrate motion-capture data with musical structure, advancing applications in dance information retrieval and multimodal interaction.