My research interests include trustworthy AI, signal processing, and data science, and it focuses on bridging stochastic dynamical systems and modern neural networks. Specifically, I am deriving stochastic stability and developing the Robust-GSR (Generalized Stochastic Resonance) theory for feature enhancement under low SNR, incorporating Wasserstein-based regularization (RGSRNet) to improve robustness and generalization in small-sample, imbalanced settings, and exploring unsupervised and interpretable feature learning inspired by stochastic resonance and optimal transport.

I am currently preparing to pursue a Ph.D. in integrating deep learning and  trustworthy AI.