I want the electromagnetic environment to be something we design in a goal-oriented way, not just something we live with — surfaces and arrays that actively shape how waves travel, for sensing, localization, and communication. My group works on three connected problems to get there: the physics, the algorithms, and the hardware.
Simulating electrically large objects, arrays, RIS, and metasurfaces accurately is still a bottleneck. We build hybrid solvers that pair the rigor of MoM, FEM, FDTD, and MLFMA with deep-learning / data-based surrogates — fast enough to live inside design and control loops.
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Future networks need to sense their surroundings and extract the information that actually matters. We build sensing pipelines on metasurfaces and phased arrays that capture task-relevant features, not every raw measurement.
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Conventional optimization chases generic numbers like SNR. We posit that the EM environment should be tuned for the actual task — and differentiable solvers make that possible, end to end.
Explore →I'm recruiting M.Sc. and Ph.D. students across all three thrusts — from EM theory and ML for solvers to RIS and phased-array hardware. If any of this pulls at you, get in touch! I welcome interested & passionate B.Sc. students as well.