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{"id":2107,"date":"2016-09-15T12:27:05","date_gmt":"2016-09-15T17:27:05","guid":{"rendered":"http:\/\/hydrogeology.glg.msu.edu\/?p=2107"},"modified":"2016-09-15T12:27:13","modified_gmt":"2016-09-15T17:27:13","slug":"tianfang-xu","status":"publish","type":"post","link":"https:\/\/hydrogeology.msu.edu\/lab-members\/tianfang-xu","title":{"rendered":"Tianfang Xu"},"content":{"rendered":"

\"20160915_114114(1)\"<\/a><\/p>\n

Research Interests<\/h3>\n

Numerical simulation and uncertainty quantification of groundwater flow and solute transport<\/p>\n

Water resources sustainability<\/p>\n

Coupled climate, hydrologic and social-economic systems<\/p>\n

Model-data fusion<\/p>\n

Machine learning<\/p>\n

 <\/p>\n

Education<\/h3>\n

Ph.D. Civil Engineering, University of Illinois at Urbana-Champaign, Jun. 2012 \u2013 Aug. 2016<\/p>\n

Thesis title: An efficient fully Bayesian approach to uncertainty quantification of groundwater models<\/p>\n

M.S. Civil Engineering, University of Illinois at Urbana-Champaign, Aug. 2010 \u2013 May. 2012<\/p>\n

Thesis title: Use of data-driven models to improve prediction of physically based groundwater models.<\/p>\n

B.S. Geotechnical Engineering, Nanjing University, China, Sep. 2006 \u2013 Jun. 2010<\/p>\n

 <\/p>\n

Publications<\/h3>\n

Xu, A. J. Valocchi, M. Ye and F. Liang. Quantifying model structural error: efficient Bayesian calibration of a regional groundwater flow model with a data-driven error model and fast surrogates. Water Resources Research, submitted.<\/p>\n

Xu and K. Guan, Temporally and spatially ranging response of rainfed corn yield to climate and extreme events in the U.S. Corn Belt, Global Change Biology, in preparation.<\/p>\n

Xu, A. J. Valocchi, M. Ye, F. Liang and Y.F. Lin. Bayesian calibration of groundwater models with input data uncertainty. Water Resources Research, in revision.<\/p>\n

Xu and A. J. Valocchi. A Bayesian approach to improved calibration and prediction of groundwater models with structural error. Water Resources Research, 51(11): 9290-9311, 2015.<\/p>\n

Xu and A. J. Valocchi. Data-driven methods to improve baseflow prediction of a regional groundwater model. Computers & Geosciences, 85(B): 124-136, 2015.<\/p>\n

Choi, J., E. Amir, T. Xu and A. J. Valocchi. Learning relational Kalman filtering. In Proc. 29th AAAI Conf. on Artificial Intelligence (AAAI-15), Austin, TX, Jan. 2015.<\/p>\n

T. Xu, A. J. Valocchi, J. Choi, and E. Amir. Use of machine learning methods to reduce predictive error of groundwater models. Groundwater, 52(3): 448-460, 2014.<\/p>\n

Complete CV<\/b><\/p>\n

CV (Last Updated September 2016)<\/a><\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

Research Interests Numerical simulation and uncertainty quantification of groundwater flow and solute transport Water resources sustainability Coupled climate, hydrologic and social-economic systems Model-data fusion Machine learning   Education Ph.D. Civil Engineering, University of Illinois at Urbana-Champaign, Jun. 2012 \u2013 Aug. 2016 Thesis title: An efficient fully Bayesian approach to uncertainty quantification of groundwater models M.S. … <\/p>\n

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