Research

My research lies at the intersection of photonics and artificial intelligence, with a focus on programmable photonic devices, inverse design, and AI-assisted electromagnetic modeling.

Programmable Photonic Devices

MEMS THz Metadevices Opto-Logic

I work on MEMS-tunable metadevice platforms for programmable spectral responses and opto-logic operation. By independently controlling electrostatically actuated elements, these devices can produce tunable resonances and EIT-like spectral features, enabling reconfigurable terahertz functionality within a compact platform.

Inverse Design for Metasurfaces

Inverse Design Metasurfaces Optimization

I am interested in inverse design methods for metasurfaces and photonic devices. Instead of relying only on forward parameter sweeping, inverse design starts from a target optical or electromagnetic response and searches for suitable physical structures, device states, or operating conditions.

AI-Assisted Electromagnetic Field Modeling

AI for Science Field Prediction Physics-Inspired Learning

My machine-learning work focuses on electromagnetic field prediction and reconstruction. I aim to incorporate physics-inspired knowledge into neural networks to improve generalization, reduce the dependence on purely data-driven modeling, and make electromagnetic prediction more efficient and robust.