I am a PhD student from Applied Mathematics at Brown University. My research focuses on Uncertainty Quantification in scientific machine learning as well as developing the most advanced scientific machine learning methods for solving PDEs. In detail, I am interested in developing new methods for uncertainty quantification in deep neural networks and applying them to solve PDEs. In summary, my research direction focus on

• Developing new uncertainty quantification methods for deep neural networks
• Developing reliable and scalable deep learning based PDE solvers
• Applying data assimilation methods for training neural networks in uncertainty quantification

This semester and last semester I had 5 courses, including Probability theory I, Probability theory II, PDE I, PDE II, and Topics on Approximation Theory.