Applied machine learning researcher specializing in inverse, constrained, and physically-consistent learning for wave-driven and complex engineered systems.
Download Full Academic CV (PDF)I am currently a doctoral researcher enrolled in the PhD programme in Electronic, Electrical and Systems Engineering at the Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, United Kingdom. Prior to this, I completed my Master's degree at the Indian Institute of Technology (IIT) Madras, where I focused on physics-guided deep learning frameworks for seismic signal processing. My doctoral research investigates learning-enabled modeling and control of reconfigurable wave-based systems for next-generation communication and sensing.
More broadly, my research focuses on inverse problems and physically grounded machine learning, where physical structure, signal representations, and domain constraints are integrated into data-driven models to improve robustness, interpretability, and generalization. I work across applied machine learning and signal processing, with applications spanning communications, sensing, seismic modeling, and imaging.
Through my publications, patents, and research artifacts, I aim to develop generalizable methodologies that transfer across disciplines rather than isolated, application-specific solutions. I actively collaborate with researchers across academia and industry and welcome discussions on inverse learning, physics-consistent ML, and signal-driven intelligent systems.
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