Zhiming Zheng

By intelligently perturbing class prototypes based on their discriminative power, VPDR achieves a superior privacy-utility trade-off in federated learning compared to naive Gaussian noise.

Transfer learning can unlock scalable emission control across diverse waste incineration plants by learning transferable system-level structures that capture physical constraints, operating-regime heterogeneity, and carbon-pollutant coupling.

LLMs can bridge the gap between heterogeneous blockchain data to detect fraud with significantly improved accuracy, even in zero-shot cross-chain scenarios.