• 模型降阶
  

• 不确定性分析

• 反问题和深度学习在图像处理中的应用

姜嘉骅于2013年毕业于中国科学技术大学数学系，后于2018年在美国麻省大学达特茅斯分校获得工程与应用科学博士学位，随后在美国弗吉尼亚理工从事博士后研究工作。她于2020年11月全职加入上海科技大学信息科学与技术学院，任助理教授（终身教授序列）、研究员、博士生导师。她的主要研究方向为模型降阶，不确定性分析，反问题和深度学习在图像处理中的应用。

1. R. Guo and J. Jiang∗, Construct Deep Neural Networks Based on Direct Sampling Methods for Solving Electrical Impedance Tomography, SIAM Journal on Scientific Computing， 2021.

2. J. Chung, E. Sturler, J. Jiang∗, Hybrid Projection Methods with Recycling for Inverse Problems, SIAM Journal on Scientific Computing, 2021.

3. Taewon Cho, Julianne Chung, J. Jiang∗, 'Hybrid Projection Methods for Large-scale Inverse Problems with Mixed Gaussian Priors, Inverse Problems', 2021.

4. J. Jiang, Y. Chen, 'Multi-level adaptive greedy algorithms for the reduced basis method, International Journal for Numerical Methods in Engineering', 2020.

5. Y. Chen, J. Jiang and A. Narayan, 'Robust Residual-based and Residual-free Greedy Algorithms for Reduced Basis Methods', Computers & Mathematics with Applications, 2019.

6. B. Dong, J. Jiang and Y. Chen, 'Optimally Convergent Hybridizable Discontinuous Galerkin Method for Fifth-order Korteweg-de Vries Type Equations', Mathematical Modeling and Numerical Analysis (ESAIM: M2AN), 2019.

7. J. Jiang, Y. Chen and A. Narayan, 'Offline-enhanced Reduced Basis Method through Adaptive Construction of the Surrogate Training Set', Journal of Scientific Computing, 2017.

8. J. Jiang, Y. Chen and A. Narayan, 'A Goal-oriented Reduced Basis Methods-accelerated Generalized Polynomial Chaos Algorithm', SIAM/ASA Journal on Uncertainty Quantification, 2016.