Sampling schedule design towards optimal drug monitoring for individualizing therapy. - Shaolin Ji; Yingzhi Zeng; Ping Wu; Edmund Jon Deoon Lee (Comput. Methods Programs Biomed., 2005)

A maximum principle for stochastic optimal control with terminal state constraints, and its applications. - Shaolin Ji; Xun Yu Zhou (Commun. Inf. Syst., 2006)

Stochastic Maximum Principle for Stochastic Recursive Optimal Control Problem Under Volatility Ambiguity. - Mingshang Hu; Shaolin Ji (SIAM J. Control. Optim., 2016)

Explicit solutions for continuous time mean-variance portfolio selection with nonlinear wealth equations. - Shaolin Ji; Xiaomin Shi (Syst. Control. Lett., 2017)

A Global Stochastic Maximum Principle for Fully Coupled Forward-Backward Stochastic Systems. - Mingshang Hu; Shaolin Ji; Xiaole Xue (SIAM J. Control. Optim., 2018)

The Existence and Uniqueness of Viscosity Solution to a Kind of Hamilton-Jacobi-Bellman Equation. - Mingshang Hu; Shaolin Ji; Xiaole Xue (SIAM J. Control. Optim., 2019)

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

Deep learning method for solving stochastic optimal control problem via stochastic maximum principle. - Shaolin Ji; Shige Peng; Ying Peng; Xichuan Zhang (CoRR, 2020)

Three Algorithms for Solving High-Dimensional Fully Coupled FBSDEs Through Deep Learning. - Shaolin Ji; Shige Peng; Ying Peng; Xichuan Zhang (IEEE Intell. Syst., 2020)

A filtering problem with uncertainty in observation. - Shaolin Ji; Chuiliu Kong; Chuanfeng Sun (Syst. Control. Lett., 2020)

A control method for solving high-dimensional Hamiltonian systems through deep neural networks. - Shaolin Ji; Shige Peng; Ying Peng; Xichuan Zhang (CoRR, 2021)

Kalman-Bucy Filtering and Minimum Mean Square Estimator under Uncertainty. - Shaolin Ji; Chuiliu Kong; Chuanfeng Sun; Ji-Feng Zhang (SIAM J. Control. Optim., 2021)

A deep learning method for solving stochastic optimal control problems driven by fully-coupled FBSDEs. - Shaolin Ji; Shige Peng; Ying Peng; Xichuan Zhang (CoRR, 2022)

Global Convergence of Successive Approximations for Non-convex Stochastic Optimal Control Problems. - Shaolin Ji; Rundong Xu (CoRR, 2022)

Maximum principle for stochastic optimal control problem of forward-backward stochastic difference systems. - Shaolin Ji; Haodong Liu (Int. J. Control, 2022)

Optimal Learning Under Robustness and Time-Consistency. - Larry G. Epstein; Shaolin Ji (Oper. Res., 2022)

Solving Stochastic Optimal Control Problem via Stochastic Maximum Principle with Deep Learning Method. - Shaolin Ji; Shige Peng; Ying Peng; Xichuan Zhang (J. Sci. Comput., 2022)

A Global Stochastic Maximum Principle for Forward-Backward Stochastic Control Systems with Quadratic Generators. - Mingshang Hu; Shaolin Ji; Rundong Xu (SIAM J. Control. Optim., 2022)

A Modified Method of Successive Approximations for Stochastic Recursive Optimal Control Problems. - Shaolin Ji; Rundong Xu (SIAM J. Control. Optim., 2022)

Solving BSDEs based on novel multi-step schemes and multilevel Monte Carlo. - Qiang Han; Shaolin Ji (J. Comput. Appl. Math., 2023)

Optimization Under Rational Expectations: A Framework of Fully Coupled Forward-Backward Stochastic Linear Quadratic Systems. - Mingshang Hu; Shaolin Ji; Xiaole Xue (Math. Oper. Res., 2023)

Novel multi-step predictor-corrector schemes for backward stochastic differential equations. - Qiang Han; Shaolin Ji (Commun. Nonlinear Sci. Numer. Simul., 2024)

A Novel Control Method for Solving High-Dimensional Hamiltonian Systems Through Deep Neural Networks. - Shaolin Ji; Shige Peng; Ying Peng; Xichuan Zhang (SIAM J. Sci. Comput., 2025)