177 Huntington Avenue
- MS in Data Science, Northeastern University
- BS in Applied Mathematics, Huazhong University of Science and Technology
- Hometown: Rizhao, China
- Field of Study: Deep Learning
- PhD Advisor: Rose Yu
What are the specifics of your graduate education (thus far)?
My research interests include spatiotemporal learning and physics-informed deep learning.
What are your research interests in a bit more detail? Is your current academic/research path what you always had in mind for yourself, or has it evolved somewhat? If so, how/why?
Currently, I am working on developing novel physics-informed deep learning models for turbulent flow prediction.
What’s one problem you’d like to solve with your research/work?
Incorporate domain knowledge, like the partial differential equations in fluid dynamics, into deep learning models.
What aspect of what you do is most interesting/fascinating to you? What aspects of your research (findings, angles, problems you’re solving) might surprise others?
Deep learning is poised to accelerate and improve fluid flow simulations because well- trained models can generate realistic instantaneous flow fields with physically accurate spatiotemporal coherence, without solving the complex nonlinear coupled PDEs that govern the system.
What are your research/career goals, going forward?
My research goal is to create novel deep learning models that incorporate domain knowledge, including physics, biology, and healthcare.