# About me

- I will join Department of Chemical and Biomolecular Engineering at University of Pennsylvania as an Assistant Professor this fall.
**PhD & Postdoc Opening**: I am looking for PhD students and Postdocs to work on scientific machine learning starting from Fall 2021. Students in chemical engineering, mechanical engineering, applied mathematics, or related majors with proficient coding skills are welcome to apply. Please feel free to contact me with CV (and/or transcripts, sample publications) attached if you are interested. For more information, please check the PhD programs in Chemical and Biomolecular Engineering and Applied Mathematics and Computational Science.- I was an Applied Mathematics Instructor in Department of Mathematics at Massachusetts Institute of Technology from 2020 to 2021. I received my Ph.D. in Division of Applied Mathematics at Brown University, working with Prof. George Em Karniadakis, in 2020. I received my B.Eng. degree in Department of Thermal Engineering at Tsinghua University in 2013.
- My current research interest is on scientific machine learning. My broad research interests focus on multiscale modeling and high performance computing.
- Curriculum Vitae

# Recent news

- Our paper on glucose levels prediction in type 2 diabetes patients via deep learning was published in npj Digital Medicine. (July 14, 2021)
- Our review paper on physics-informed machine learning was published in Nature Reviews Physics. (May 24, 2021)
- I gave a talk on DeepONet at SIAM Conference on Applications of Dynamical Systems. (May 24, 2021)
- We used DeepONet to predict linear instability waves in high-speed boundary layers. (May 18, 2021)
- One-shot learning for PDEs solution operators was presented at ICLR Workshop on Deep Learning for Simulation. (May 7, 2021)
- I gave a talk on DeepONet in the Numerical Analysis Seminar at University of Iowa. (May 4, 2021)
- News of DeepONet on Quanta Magazine. (Apr. 19, 2021)
- News of DeepONet on Tech Xplore. (Apr. 12, 2021)
- We proposed the first method of one-shot learning for solution operators of partial differential equations. (Apr. 12, 2021)
- DeepONet was published in Nature Machine Intelligence, and also see the News article. (Mar. 18, 2021)
- I gave a talk on DeepONet at SIAM Conference on Computational Science and Engineering. (Mar. 3, 2021)
- I gave a talk on machine learning & multiscale modeling at Purdue University, Department of Mathematics. (Feb. 22, 2021)
- I gave a talk on machine learning & multiscale modeling at Queen’s University, Department of Mechanical and Material Engineering. (Feb. 10, 2021)
- We developed hPINN for inverse design/topology optimization. (Feb. 9, 2021)
- Our paper on DeepXDE was published in SIAM Review. (Feb. 4, 2021)
- I gave a talk on machine learning & multiscale modeling at University of Pennsylvania, Department of Chemical and Biomolecular Engineering. (Feb. 3, 2021)
- Our systems-biology PINN is highlighted on Nature Computational Science. (Jan. 14, 2021)
- We used DeepONet for predicting multiscale bubble growth dynamics. (Dec. 23, 2020)
- Our paper on PINN for systems biology was published in PLOS Computational Biology. (Nov. 18, 2020)
- Our paper on dying ReLU was published in Communications in Computational Physics. (Nov. 18, 2020)
- We developed DeepM&Mnet for hypersonics. (Nov. 1, 2020)
- We developed DeepM&Mnet to build multiphysics & multiscale models in a plug-and-play mode using pretrained DeepONets that approximate nonlinear operators. (Sept. 28, 2020)
- I received Chinese Government Award for Outstanding Self-Financed Students Abroad. (July 10, 2020)
- I received Joukowsky Family Foundation Outstanding Dissertation Award (the most prestigious Ph.D. award) from Brown University. (May 24, 2020)
- I received David Gottlieb Memorial Award from the Division of Applied Mathematics, Brown University. (Apr. 1, 2020)