7.国家自然科学基金面上项目(42472321)，基于数据和模型驱动的变化环境下毛乌素沙地地下水循环演变机制研究，50万，2025.01-2028.12，主持

6.南京大学准聘助理教授岗位启动经费，2023-2026，主持

5.“科技兴蒙”重点专项(2021EEDSCXSFQZD010)，基于生态安全的毛乌素沙地水资源集约高效利用技术与示范，子课题：群矿联采条件下煤层水系统互扰机制与疏干水精准模拟预测技术，80万，2021.11-2024.05，主持

4.国家自然科学基金青年项目(42002248)，基于深度卷积长短期记忆网络的地下水污染源和高维非均质含水层参数场联合反演研究，24万，2021.01-2023.12，主持

3.中国博士后科学基金面上项目(2020M681550)，基于深度学习的地下水污染溯源和修复方案优化研究，8万，2020.12-2022.11，主持

2.江苏省博士后科研资助计划项目(2020Z133)，基于深度学习的DNAPL污染修复方案优化设计研究，5万，2020.08-2022.11，主持

1.国家级大学生创新性实验计划项目(G1210284016)，苏北盆地CO2咸水层地质封存水-岩作用初步研究，1.2万，2012.04-2013.05，主持

• 学术论文：

Google Scholar: https://scholar.google.com/citations?user=b5m_q4sAAAAJ&hl=en&oi=ao

(*Corresponding authorship/通讯作者)

15. Peng, Z., Mo, S.*, Sun, A. Y., Wu, J.*, Zeng, X., Lu, M., Shi, X. (2025). An explainable Bayesian TimesNet for probabilistic groundwater level prediction. Water Resources Research, 61(6), e2025WR040191.

14. Kang, J., Shi, X., Mo, S., Sun, A. Y., Wang, L., Wang, H., Wu, J. (2025). Leakage risk assessment in geologic carbon sequestration using a physics-aware ConvLSTM surrogate model. Advances in Water Resources, 105017.

13. Mo, S., Schumacher, M., van Dijk, A. I., Shi, X., Wu, J., Forootan, E. (2025). Near-real-time monitoring of global terrestrial water storage anomalies and hydrological droughts. Geophysical Research Letters, 52(7), e2024GL112677.

12. Feng, L., Mo, S.*, Sun, A. Y., …, Wu, J., Shi, X. (2024). Deep learning-based geological parameterization for history matching CO2 plume migration in complex aquifers. Advances in Water Resources, 193, 104833.

11. Feng, L., Mo, S.*, Sun, A. Y., Wu, J., Shi, X. (2024). Uncertainty quantification of CO2 plume migration in highly channelized aquifers using probabilistic convolutional neural networks. Advances in Water Resources, 183, 104607.

10. Hu, Z., Tang, S., Mo, S.*, Shi, X.*, Yin, X., Sun, Y., Liu, X., Duan, L., Miao, P., Liu, T., Wu, J. (2024). Water storage changes (2003–2020) in the Ordos Basin, China, explained by GRACE data and interpretable deep learning. Hydrogeology Journal, 1-14.

9. Mo, S., Zhong, Y., Forootan, E., Shi, X., Feng, W., Yin, X., Wu, J. (2022). Hydrological droughts of 2017-2018 explained by the Bayesian reconstruction of GRACE(-FO) fields. Water Resources Research, 58, e2022WR031997.

8. Mo, S., Zhong, Y., Forootan, E., Mehrnegar, N., Yin, X., Wu, J., Feng, W., Shi, X. (2022). Bayesian convolutional neural networks for predicting the terrestrial water storage anomalies during GRACE and GRACE-FO gap. Journal of Hydrology, 604, 127244.

7. Du, J., Shi, X.*, Mo, S.*, Kang, X., Wu, J., 2022. Deep learning based optimization under uncertainty for surfactant-enhanced DNAPL remediation in highly heterogeneous aquifers. Journal of Hydrology, 608, 127639.

6. Kang, X., Kokkinaki, A., Kitanidis, P. K., Shi, X., Lee, J., Mo, S., Wu, J. (2021). Hydrogeophysical characterization of nonstationary DNAPL source zones by integrating a convolutional variational autoencoder and ensemble smoother. Water Resources Research, e2020WR028538.

5. Mo, S., Zabaras, N., Shi, X., Wu, J. (2020). Integration of adversarial autoencoders with residual dense convolutional networks for estimation of non-Gaussian hydraulic conductivities. Water Resources Research, 56(2), e2019WR026082.

4. Mo, S., Zabaras, N., Shi, X., Wu, J. (2019). Deep autoregressive networks for high-dimensional inverse problems in groundwater contaminant source identification. Water Resources Research, 55(5), 3856-3881. (2018-2019年度期刊高下载文章)

3. Mo, S., Shi, X., Lu, D., Ye, M., Wu, J. (2019). An adaptive Kriging surrogate method for efficient uncertainty quantification with an application to geological carbon sequestration modeling. Computers & Geosciences, 125, 69-77.

2. Mo, S., Zhu, Y., Zabaras, N., Shi, X., Wu, J. (2019). Deep convolutional encoder-ecoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media. Water Resources Research, 55(1), 703-728. (ESI 高被引论文)

1. Mo, S., Lu, D., Shi, X., Zhang, G., Ye, M., Wu, J. F., Wu, J. C. (2017). A Taylor expansion-based adaptive design strategy for global surrogate modeling with applications in groundwater modeling. Water Resources Research, 53(12), 10802-10823.

• 发明专利：

8.莫绍星，冯立，施小清，康学远，吴吉春，国家发明专利，基于深度学习参数化策略的C02地质封存反演模拟方法，2025-06-06，ZL202411582981.5

7. 施小清，莫绍星，杜建要，康学远，国家发明专利，一种不确定性条件下DNAPL污染场地修复的多目标优化方法，2025-05-23，ZL202210025261.3

6.莫绍星，彭泽辰，吴吉春，施小清，曾献奎, 国家发明专利，一种基于可解释贝叶斯卷积网络的地下水位概率预报方法, 2024-05-31, ZL202410339570.7

5.莫绍星, 冯立, 施小清, 吴吉春, 国家发明专利，一种基于贝叶斯深度学习的碳封存模型不确定性分析方法, 2024-05-31, ZL202410339656.X

4.施小清，康学远，莫绍星，吴吉春,徐红霞，国家发明专利，基于卷积神经网络识别DNAPL污染物在地下含水层分布的方法，2024-03-22，ZL202011014665.X

3.莫绍星, 胡子鸣, 施小清, 吴吉春, 尹鑫, 孙媛媛, 国家发明专利，基于可解释深度学习的区域陆地水储量变化归因分析方法, 2024-02-02, ZL202311372396.8

2.施小清，马春龙，莫绍星，徐红霞，吴吉春，国家发明专利，基于数据挖掘的场地污染特征因子识别和监测指标优化方法，2023-11-21，ZL202011182291.2

1.莫绍星, 施小清, 吴吉春, 国家发明专利，一种基于卷积生成对抗网络的含水层参数场反演方法, 2022-09-06,ZL202210738233.6

• 软件著作权：

3.莫绍星，胡子鸣，彭泽辰，施小清，吴吉春，刘晓民，田小强，杨耀天，计算机软件著作权登记授权，基于参数-边界联动技术的群矿联采扰动下煤矿疏干水精准模拟预测软件[简称：PB-CMD]V1.0，2024-08-01，2024SR1102770

2.莫绍星，冯立，施小清，吴吉春，计算机软件著作权登记授权，基于贝叶斯卷积神经网络的CO2驱油封存提高采收率(CO2-EOR)的高效替代模拟软件[简称：BCNN-CO2-EOR] V1.0，2023-08-28，2023SR0952486

1.莫绍星，杜建雯，施小清，康学远，吴吉春，计算机软件著作权登记授权，地下水模拟的深度学习高效仿真替代程序软件（简称DL-GS V1.0），2022-02-14，2022SR0225987

• 已毕业研究生（含共同指导）：