Pranav Agrawal

My interdisciplinary research focuses on vibroacoustics, advancing signal processing, optimization and machine learning to address challenges in critical infrastructure and dynamics systems, such as water distribution networks (WDNs), space structures, defense and automotive systems. In my postdoctoral research, I have developed novel acoustic-based methods for leak localization in complex WDNs, integrating graph theory, maximum likelihood estimation, and AI-driven approaches to enhance accuracy and sensitivity. My research also includes optimal sensor placement strategies using genetic algorithms and pioneering active transducer techniques for proactive leak detection. Beyond WDNs, I apply my knowledge to battery health estimation, developing robust CNN-LSTM models to predict State of Health and Remaining Useful Life for lithium-ion batteries, supporting sustainable energy systems. Additionally, my PhD research on cable-harnessed structures has led to novel analytical models and damping mechanisms, validated through experimental modal analysis, with applications in aerospace engineering.

​Currently, I am a postdoctoral researcher in Sriram Narasimhan's group at University of California Los Angeles (UCLA). Previously, I was a doctoral researcher at University of Waterloo (UW) in Vibration and Energy harvesting lab led by Armaghan Salehian. Also, I held a sessional instructor position at UW, developing a graduate course curriculum on Engineering Acoustics, and using a new teaching and learning methods for teaching junior-level Dynamics course. Before my PhD, I worked as a Noise Vibration and Harshness (NVH) engineer in research division of Tata Motors Limited. I obtained a Master's-Bachelor's dual degree from Indian Institute of Technology (IIT) Kanpur specializing in engineering design, vibrations and nanomechanics, and supervised by Shakti Gupta.