Link to Google Scholar Page and Research gate Profile. 

1. X. Ren, Z. Zhang, J. Jiang, G. Li, J. Jiang, X. Jia, Deep Learning Instance Segmentation on Esophageal Squamous Cell Carcinoma Detection, submitted, [medRxiv]

2. W. Ren, S. Shen, L. Li, S. Gao, Y. Wang, L. Gu, S. Li, X. Zhu, J. Jiang*, J. Yu*, High-resolution Tomographic Reconstruction of Optical Absorbance Using Neural Fields, submitted.

3. J. Chung, J. Jiang*, SM. Miller, AK. Saibaba, Hybrid Projection Methods for Solution Decomposition in Large-scale Bayesian Inverse Problems, submitted, [arXiv:2206.06664].

4. R. Guo, Y. Li, J. Jiang*，Learn An Index Operator by CNN for Solving Diffusive Optical Tomography: A Deep Direct Sampling Method, submitted, [arXiv:2104.07703].

5. Z. Guo, X. Ren, J. Jiang, H. Liu, Y. Huo, X. Zhao, K. N. TuAkil, Y. Liu, The Design of Eutectic High Entropy Solder by Machine Learning, submitted.

6. E. Qian, J. Tabeart, C. Beattie, S. Gugercin, J. Jiang, P. Kramer, Akil Narayan, Model Reduction of Linear Dynamical Systems via Balancing for Bayesian Inference, Journal of Scientific Computing, 91(1), pp.1-30., 2022. [arXiv:2111.13246].

7. J. Lin, C. Jiang, J. Jiang, Conjugate Gradient Persymmetric Adaptive Matched Filter, Digital Signal Processing, 123, p.103395., 2022. 

8. J. Jiang*, Fatoumata Sanogo, Carmeliza Navasca, Low-CP-rank Tensor Completion via Practical Regularization, Journal of Scientific Computing, 91(1), pp.1-20, 2022.

9. R. Guo, J. Jiang*, Construct Deep Neural Networks Based on Direct Sampling Methods for Solving Electrical Impedance Tomography, SIAM Journal on Scientific Computing, 43 (3), B678-B711, 2021. [arXiv:2009.08024] 

10. J. Chung, E. Sturler, J. Jiang*, Hybrid Projection Methods with Recycling for Inverse Problems, SIAM Journal on Scientific Computing, S146-S172, 2021. [arXiv:2007.00207] 

11. T. Cho, J. Chung, J. Jiang*, Hybrid Projection Methods for Large-scale Inverse Problems with Mixed Gaussian Priors, Inverse Problems, 37(4), pp. 044002, 2021. [arXiv:2003.13766] 

12. J. Jiang, Y. Chen, Multi-level Adaptive Greedy Algorithms for the Reduced Basis Method, International Journal for Numerical Methods in Engineering,121 (23), pp. 5426-5445, 2020. [arXiv:1911.02214] 

13. Y. Chen, J. Jiang and A. Narayan, Robust Residual-based and Residual-free Greedy Algorithms for Reduced Basis Methods, Computers & Mathematics with Applications, 77(7), pp. 1963-1979, 2019. [arXiv:1710.08999] 

14. B. Dong, J. Jiang, Y. Chen, Optimally Convergent Hybridizable Discontinuous Galerkin Method for Fifth-order Korteweg-de Vries Type Equations, Mathematical Modeling and Numerical Analysis (ESAIM: M2AN), 52(6), pp. 2283 - 2306, 2019. [arXiv:1710.07734] 

15. J. Jiang, Y. Chen, A. Narayan, Offline-enhanced Reduced Basis Method through Adaptive Construction of the Surrogate Training Set, Journal of Scientific Computing, 73(2-3), pp. 853–875, 2017. [arXiv:1703.05683] 

16. J. Jiang, Y. Chen, A. Narayan, A Goal-oriented Reduced Basis Methods-accelerated Generalized Polynomial Chaos Algorithm, SIAM/ASA Journal on Uncertainty Quantification, 4(1), pp. 1398-1420, 2016. [arXiv:1601.00137]