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. 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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