本科和博士皆畢業(yè)于浙江大學(xué),，主要從事水文系統(tǒng)數(shù)值建模、深度學(xué)習(xí)和數(shù)據(jù)同化等方面的研究,。在Water Resources Research（WRR）,，Journal of Hydrology（JoH）等水文水資源領(lǐng)域主流期刊發(fā)表多篇論文，其中在本領(lǐng)域頂級(jí)期刊WRR發(fā)表第一作者論文8篇,，主持或參與中國博士后科學(xué)基金,、國家自然科學(xué)基金等多個(gè)項(xiàng)目。擔(dān)任WRR,、JoH等期刊審稿人,。

教育經(jīng)歷

• 2011.08 – 2017.06：浙江大學(xué)，土壤學(xué),，博士
• 2007.09 – 2011.06：浙江大學(xué),，資源環(huán)境科學(xué)，學(xué)士

工作經(jīng)歷

• 2020.08 – 至今：河海大學(xué)長江保護(hù)與綠色發(fā)展研究院,，教授
• 2017.07 – 2020.07：浙江大學(xué),，博士后
• 2016.05 – 2016.12：美國普渡大學(xué)數(shù)學(xué)系，訪問學(xué)者
• 2015.11 – 2016.05：美國西北太平洋國家實(shí)驗(yàn)室，訪問學(xué)者

研究方向

• 水文系統(tǒng)數(shù)值建模
• 深度學(xué)習(xí)
• 數(shù)據(jù)同化

代表性論著

• Zhang, J., Zeng, L., Chen, C., Chen, D., & Wu, L. (2015). Efficient Bayesian experimental design for contaminant source identification. Water Resources Research, 51(1), 576–598. https://doi.org/10.1002/2014WR015740
• Zhang, J., Li, W., Zeng, L., & Wu, L. (2016). An adaptive Gaussian process-based method for efficient Bayesian experimental design in groundwater contaminant source identification problems. Water Resources Research, 52(8), 5971–5984. https://doi.org/10.1002/2016WR018598
• Zhang, J., Man, J., Lin, G., Wu, L., & Zeng, L. (2018). Inverse Modeling of Hydrologic Systems with Adaptive Multifidelity Markov Chain Monte Carlo Simulations. Water Resources Research, 54(7), 4867–4886. https://doi.org/10.1029/2018WR022658
• Zhang, J., Vrugt, J. A., Shi, X., Lin, G., Wu, L., & Zeng, L. (2020). Improving simulation efficiency of MCMC for inverse modeling of hydrologic systems with a kalman‐inspired proposal distribution. Water Resources Research, 56(3). https://doi.org/10.1029/2019WR025474
• Zhang J, Zheng Q, Wu L, Zeng L.(2020). Using Deep Learning to Improve Ensemble Smoother: Applications to Subsurface Characterization. Water Resources Research. 56(12), e2020WR027399. https://doi.org/10.1029/2020WR027399