Biography：

(1) 2019.12-ongoing, Institute of Soil Science, Chinese Academy of Sciences, Research Associate

(2) 2016.09-2019.12, Zhejiang University, Ph.D. in Soil Sciences

(3) 2018.08-2019.08, Purdue University, Visiting Scholar in Department of Mathematics

(4) 2013.09-2016.06, Zhejiang University, M.Sc. in Utilization of Agricultural Resource

(5) 2009.09-2013.06, Northwest A&F University, B.Sc. in Science of Natural Resources and Environment

Research Areas：

Subsurface flow modeling; Machine learning

Public Duties：

(1) Member of American Geophysical Union

(2) Reviewers of Advances in Water Resources and Stochastic Environmental Research and Risk Assessment

Awards and Honours：

(1) Jiangsu Innovative and Entrepreneurial Doctor (2020)

(2) Zhejiang University Academic Award for Outstanding Doctoral Candidates (2018)

(3) National Scholarship for Doctoral Students (2018)

Selected Publications：

(1) Man Jun; Zheng Qiang; Wu Laosheng; Zeng Lingzao*. Improving parameter estimation with an efficient sequential probabilistic collocation-based optimal design method. Journal of Hydrology, 2019, 569: 1-11.

(2) Man Jun; Zhang Jiangjiang; Wu Laosheng; Zeng Lingzao*. ANOVA-based multi-fidelity probabilistic collocation method for uncertainty quantification. Advances in Water Resources, 2018, 122: 176-186.

(3) Man Jun; Liao Qinzhuo; Zeng Lingzao*; Wu Laosheng. ANOVA-based transformed probabilistic collocation method for Bayesian data-worth analysis. Advances in Water Resources, 2017, 110: 203-214.

(4) Man Jun; Zhang Jiangjiang; Li Weixuan; Zeng Lingzao*; Wu Laosheng. Sequential ensemble-based optimal design for parameter estimation. Water Resources Research, 2016, 52(10): 7577-7592.

(5) Man Jun; Li Weixuan; Zeng Lingzao*; Wu Laosheng. Data assimilation for unsaturated flow models with restart adaptive probabilistic collocation based Kalman filter. Advances in Water Resources, 2016, 92: 258-270.

(6) Man Jun; Zheng Qiang; Wu Laosheng; Zeng Lingzao*. Adaptive multi-fidelity probabilistic collocation-based Kalman filter for subsurface flow data assimilation: numerical modeling and real-world experiment. Stochastic Environmental Research and Risk Assessment, 2020, 34: 1135-1146.

Representive Projetcs：

(1) Special Research Associate Program of the Chinese Academy of Sciences, 2019.12-2022.12, in charge

(2) Jiangsu Natural Science Foundation, Development and Application of Data Assimilation Methods in Vapor Intrusion of Contaminated Sites, 2020.07-2023.06, in charge

(3) Jiangsu Postdoctoral Science Foundation, 2020.06-2022.05, in charge

(4) National Natural Science Foundation of China, Inversion of Unsaturated Soil Hydraulic Parameters via Heat Tracing, 2018.01-2021.12, in participation