1. Larry G. Epstein and Shaolin Ji, Optimal Learning Under Robustness and Time-Consistency, Operations Research, 70(3): 1317-1329, 2022.

2. Shaolin Ji, Shige Peng, Ying Peng and Xichuan Zhang，Solving stochastic optimal control problem via stochastic maximum principle with deep learning method，Journal of scientific computing, 2023.

3. Qiang Han and Shaolin Ji, Solving BSDEs based on novel multi-step schemes and multilevel Monte Carlo, Journal of Computational and Applied Mathematics (2023).

4. Shaolin Ji, Shige Peng, Ying Peng, and Xichuan Zhang, Three Algorithms for Solving High-Dimensional Fully Coupled FBSDEs Through Deep Learning, IEEE Intelligent Systems, 35(3) May-June 1 (2020), 71-84.

1. Larry G. Epstein and Shaolin Ji, Ambiguous Volatility and Asset Pricing in Continuous Time, The Review of Financial Studies, 26 (7): 1740-1786, 2013.

2. Larry G. Epstein and Shaolin Ji, Ambiguous volatility, possibility and utility in continuous time, Journal of Mathematical Economics, 50: 269-282, 2014.

3. Carole Bernard, Shaolin Ji and Weidong Tian, An optimal insurance design problem under Knightian uncertainty, Decisions in economics and finance, 36(2): 99-124, 2013.

4. Shaolin ji, Li Li and Jianjun Miao, Dynamic Contracts with Learning Under Ambiguity, Preprint (download), 2016.

1. Shaolin Ji and Shige Peng, Terminal perturbation method for the backward approach to continuous time mean-variance portfolio selection, Stochastic processes and their Applications, 118(6): 952-967, 2008.

2. Shaolin Ji and Xun Yu Zhou, A generalized Neyman–Pearson lemma under g-probabilities, Probability theory and related fields, 148: 645-669, 2010.

3. Mingshang Hu, Shaolin Ji, Shige Peng, Yongsheng Song, Backward stochastic differential equations driven by G-Brownian motion, Stochastic Processes and their Applications, 124(1): 759–784, 2014.

4. Mingshang Hu, Shaolin Ji, Shige Peng, Yongsheng Song, Comparison theorem, Feynman–Kac formula and Girsanov transformation for BSDEs driven by G-Brownian motion, Stochastic Processes and their Applications, 124(2): 1170–1195, 2014.

1. Mingshang Hu and Shaolin Ji Stochastic maximum principle for stochastic recursive optimal control problem under volatility uncertainty, SIAM Journal on Control and Optimization 54(2):918-945, 2016.

2. Mingshang Hu, Shaolin Ji and Xiaole Xue, A Global stochastic maximum principle for fully coupled forward-backward stochastic systems, SIAM Journal on Control and Optimization 56(6): 4309-4335, 2018.

3. Mingshang Hu, Shaolin Ji and Xiaole Xue,  The Existence and Uniqueness of Viscosity Solution to a Kind of Hamilton–Jacobi–Bellman Equation. SIAM Journal on Control and Optimization 57 (2019), no. 6, 3911–3938.

4. Ji, Shaolin; Kong, Chuiliu; Sun, Chuanfeng; A robust Kalman-Bucy filtering problem. Automatica 122 (2020).

5. Shaolin Ji, Chuiliu Kong, Chuanfeng Sun and Jifeng Zhang, Kalman-Bucy filtering and minimum mean square estimator under uncertainty, SIAM Journal on Control and Optimization 59(4): 2669–2692, 2021.

6. Mingshang Hu, Shaolin Ji, and Rundong Xu，A Global Stochastic Maximum Principle for Forward-Backward Stochastic Control Systems with Quadratic Generators，SIAM Journal on Control and Optimization，60(3)，2022.

7. Shaolin Ji, and Rundong Xu，A Modified Method of Successive Approximations for Forward-Backward Stochastic Control Systems，SIAM Journal on Control and Optimization，2022.

8. Shaolin Ji and Xun Yu Zhou, A maximum principle for stochastic optimal control with terminal state constraints, and its applications, A special issue dedicated Tyrone Duncan on the occation of his 65th birthday, Communications in Information and Systems, 6(4): 321-338, 2006.

9. Mingshang Hu, Shaolin Ji and Shuzhen Yang A Stochastic Recursive Optimal Control Problem Under the G-expectation Framework，Applied Mathematics and Optimization, 70(2): 253-278, 2014.

10. Mingshang Hu and Shaolin Ji, Dynamic programming principle for stochastic recursive optimal control problem driven by a G-Brownian motion, Stochastic Processes and their Applications 127 (2017) 107–1.

1. Larry G. Epstein and Shaolin Ji, Optimal Learning Under Robustness and Time-Consistency, Operations Research, 70(3): 1317-1329, 2022.

2. Shaolin Ji, Shige Peng, Ying Peng and Xichuan Zhang，Solving stochastic optimal control problem via stochastic maximum principle with deep learning method，Journal of scientific computing, 2023.

3. Qiang Han and Shaolin Ji, Solving BSDEs based on novel multi-step schemes and multilevel Monte Carlo, Journal of Computational and Applied Mathematics (2023).

4. Shaolin Ji, Shige Peng, Ying Peng, and Xichuan Zhang, Three Algorithms for Solving High-Dimensional Fully Coupled FBSDEs Through Deep Learning, IEEE Intelligent Systems, 35(3) May-June 1 (2020), 71-84.

1. Larry G. Epstein and Shaolin Ji, Ambiguous Volatility and Asset Pricing in Continuous Time, The Review of Financial Studies, 26 (7): 1740-1786, 2013.

2. Larry G. Epstein and Shaolin Ji, Ambiguous volatility, possibility and utility in continuous time, Journal of Mathematical Economics, 50: 269-282, 2014.

3. Carole Bernard, Shaolin Ji and Weidong Tian, An optimal insurance design problem under Knightian uncertainty, Decisions in economics and finance, 36(2): 99-124, 2013.

4. Shaolin ji, Li Li and Jianjun Miao, Dynamic Contracts with Learning Under Ambiguity, Preprint (download), 2016.

1. Shaolin Ji and Shige Peng, Terminal perturbation method for the backward approach to continuous time mean-variance portfolio selection, Stochastic processes and their Applications, 118(6): 952-967, 2008.

2. Shaolin Ji and Xun Yu Zhou, A generalized Neyman–Pearson lemma under g-probabilities, Probability theory and related fields, 148: 645-669, 2010.

3. Mingshang Hu, Shaolin Ji, Shige Peng, Yongsheng Song, Backward stochastic differential equations driven by G-Brownian motion, Stochastic Processes and their Applications, 124(1): 759–784, 2014.

4. Mingshang Hu, Shaolin Ji, Shige Peng, Yongsheng Song, Comparison theorem, Feynman–Kac formula and Girsanov transformation for BSDEs driven by G-Brownian motion, Stochastic Processes and their Applications, 124(2): 1170–1195, 2014.

1. Mingshang Hu and Shaolin Ji Stochastic maximum principle for stochastic recursive optimal control problem under volatility uncertainty, SIAM Journal on Control and Optimization 54(2):918-945, 2016.

2. Mingshang Hu, Shaolin Ji and Xiaole Xue, A Global stochastic maximum principle for fully coupled forward-backward stochastic systems, SIAM Journal on Control and Optimization 56(6): 4309-4335, 2018.

3. Mingshang Hu, Shaolin Ji and Xiaole Xue,  The Existence and Uniqueness of Viscosity Solution to a Kind of Hamilton–Jacobi–Bellman Equation. SIAM Journal on Control and Optimization 57 (2019), no. 6, 3911–3938.

4. Ji, Shaolin; Kong, Chuiliu; Sun, Chuanfeng; A robust Kalman-Bucy filtering problem. Automatica 122 (2020).

5. Shaolin Ji, Chuiliu Kong, Chuanfeng Sun and Jifeng Zhang, Kalman-Bucy filtering and minimum mean square estimator under uncertainty, SIAM Journal on Control and Optimization 59(4): 2669–2692, 2021.

6. Mingshang Hu, Shaolin Ji, and Rundong Xu，A Global Stochastic Maximum Principle for Forward-Backward Stochastic Control Systems with Quadratic Generators，SIAM Journal on Control and Optimization，60(3)，2022.

7. Shaolin Ji, and Rundong Xu，A Modified Method of Successive Approximations for Forward-Backward Stochastic Control Systems，SIAM Journal on Control and Optimization，2022.

8. Shaolin Ji and Xun Yu Zhou, A maximum principle for stochastic optimal control with terminal state constraints, and its applications, A special issue dedicated Tyrone Duncan on the occation of his 65th birthday, Communications in Information and Systems, 6(4): 321-338, 2006.

9. Mingshang Hu, Shaolin Ji and Shuzhen Yang A Stochastic Recursive Optimal Control Problem Under the G-expectation Framework，Applied Mathematics and Optimization, 70(2): 253-278, 2014.

10. Mingshang Hu and Shaolin Ji, Dynamic programming principle for stochastic recursive optimal control problem driven by a G-Brownian motion, Stochastic Processes and their Applications 127 (2017) 107–1.

1. Larry G. Epstein and Shaolin Ji, Optimal Learning Under Robustness and Time-Consistency, Operations Research, 70(3): 1317-1329, 2022.

2. Shaolin Ji, Shige Peng, Ying Peng and Xichuan Zhang，Solving stochastic optimal control problem via stochastic maximum principle with deep learning method，Journal of scientific computing, 2023.

3. Qiang Han and Shaolin Ji, Solving BSDEs based on novel multi-step schemes and multilevel Monte Carlo, Journal of Computational and Applied Mathematics (2023).

4. Shaolin Ji, Shige Peng, Ying Peng, and Xichuan Zhang, Three Algorithms for Solving High-Dimensional Fully Coupled FBSDEs Through Deep Learning, IEEE Intelligent Systems, 35(3) May-June 1 (2020), 71-84.

1. Larry G. Epstein and Shaolin Ji, Ambiguous Volatility and Asset Pricing in Continuous Time, The Review of Financial Studies, 26 (7): 1740-1786, 2013.

2. Larry G. Epstein and Shaolin Ji, Ambiguous volatility, possibility and utility in continuous time, Journal of Mathematical Economics, 50: 269-282, 2014.

3. Carole Bernard, Shaolin Ji and Weidong Tian, An optimal insurance design problem under Knightian uncertainty, Decisions in economics and finance, 36(2): 99-124, 2013.

4. Shaolin ji, Li Li and Jianjun Miao, Dynamic Contracts with Learning Under Ambiguity, Preprint (download), 2016.

1. Shaolin Ji and Shige Peng, Terminal perturbation method for the backward approach to continuous time mean-variance portfolio selection, Stochastic processes and their Applications, 118(6): 952-967, 2008.

2. Shaolin Ji and Xun Yu Zhou, A generalized Neyman–Pearson lemma under g-probabilities, Probability theory and related fields, 148: 645-669, 2010.

3. Mingshang Hu, Shaolin Ji, Shige Peng, Yongsheng Song, Backward stochastic differential equations driven by G-Brownian motion, Stochastic Processes and their Applications, 124(1): 759–784, 2014.

4. Mingshang Hu, Shaolin Ji, Shige Peng, Yongsheng Song, Comparison theorem, Feynman–Kac formula and Girsanov transformation for BSDEs driven by G-Brownian motion, Stochastic Processes and their Applications, 124(2): 1170–1195, 2014.

1. Mingshang Hu and Shaolin Ji Stochastic maximum principle for stochastic recursive optimal control problem under volatility uncertainty, SIAM Journal on Control and Optimization 54(2):918-945, 2016.

2. Mingshang Hu, Shaolin Ji and Xiaole Xue, A Global stochastic maximum principle for fully coupled forward-backward stochastic systems, SIAM Journal on Control and Optimization 56(6): 4309-4335, 2018.

3. Mingshang Hu, Shaolin Ji and Xiaole Xue,  The Existence and Uniqueness of Viscosity Solution to a Kind of Hamilton–Jacobi–Bellman Equation. SIAM Journal on Control and Optimization 57 (2019), no. 6, 3911–3938.

4. Ji, Shaolin; Kong, Chuiliu; Sun, Chuanfeng; A robust Kalman-Bucy filtering problem. Automatica 122 (2020).

5. Shaolin Ji, Chuiliu Kong, Chuanfeng Sun and Jifeng Zhang, Kalman-Bucy filtering and minimum mean square estimator under uncertainty, SIAM Journal on Control and Optimization 59(4): 2669–2692, 2021.

6. Mingshang Hu, Shaolin Ji, and Rundong Xu，A Global Stochastic Maximum Principle for Forward-Backward Stochastic Control Systems with Quadratic Generators，SIAM Journal on Control and Optimization，60(3)，2022.

7. Shaolin Ji, and Rundong Xu，A Modified Method of Successive Approximations for Forward-Backward Stochastic Control Systems，SIAM Journal on Control and Optimization，2022.

8. Shaolin Ji and Xun Yu Zhou, A maximum principle for stochastic optimal control with terminal state constraints, and its applications, A special issue dedicated Tyrone Duncan on the occation of his 65th birthday, Communications in Information and Systems, 6(4): 321-338, 2006.

9. Mingshang Hu, Shaolin Ji and Shuzhen Yang A Stochastic Recursive Optimal Control Problem Under the G-expectation Framework，Applied Mathematics and Optimization, 70(2): 253-278, 2014.

10. Mingshang Hu and Shaolin Ji, Dynamic programming principle for stochastic recursive optimal control problem driven by a G-Brownian motion, Stochastic Processes and their Applications 127 (2017) 107–1.