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177 Huntington Avenue


  • MS in Data Science, Northeastern University
  • BS in Applied Mathematics, Huazhong University of Science and Technology

About Me

  • 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.