I am an applied machine learning researcher working on inverse, constrained, and physics-consistent learning for wave-driven and complex engineered systems. My research focuses on integrating physical structure, signal representations, and domain constraints into machine learning models to improve robustness, interpretability, and data efficiency.

I am currently pursuing a PhD in Electronic, Electrical and Systems Engineering at Loughborough University (UK). My doctoral work explores learning-enabled modeling and control of reconfigurable wave-based systems for next-generation communication and sensing, while my broader research agenda addresses inverse learning and physically grounded machine intelligence across multiple application domains.

My interests span applied ML, signal processing, inverse problems, and wave-based systems, with applications including communications, sensing, seismic modeling, and imaging. I am particularly interested in research that bridges theory, systems, and deployment.

Feel free to reach out for collaborations or knowledge exchange.

It’s always a good time to say Hello! 👋🏽🙂📩