My research topics focus on AI4Science, particularly on neural operators and applications. The goal is to enpower engineering simulations by AI technology, e.g., fluid dynamics, electromagnetism, diffusion-reaction, etc.

Research in explainable and robust deep learning

My work focuses on using machine learning techniques to solve problems in manufacturing industry. This involves end-to-end tasks such as problem definition, experiment design, signal and other data preprocessing, model design and training, etc. I use suitable AI technologies, e.g. boosted tree, variational autoencoder, etc. to solve problems at different scales. In terms of goals, the categories of problems I work on include prediction, classification and simulation. Also, my work involves research on deep learning, e.g. the disengagement of latent variables towards a better representative learning, etc.

My research focus on the qualitative analysis of semilinear systems with fractional Laplacians.I also teach some college math courses, e.g. Calculus I, College Algebra.