My Projects

Physcis Guided Machine Learning (NSF)

• Incorporated the laws of physics into deep learning models to improve their interpretability and generalization
• Developed Taylor Neural Networks without using activation functions
• Apply the proposed models to autonomous systems, AD, and climate changes

Concept-based Interpretable Machine Learning

• Proposed a novel ControlVAE that combines control theory with Variational Autoencoders to disentangle the latent factors.
• Adopted disentangled latent factors to explain the prediction results

Privacy-Preserving Federated Learning

• Developed data-free one-shot federated learning for heterogeneous data
• Developed adaptive gradient protection method to achieve a good trade-off between interpretability and privacy protection