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 – Aug. 2016
Thesis title: An efficient fully Bayesian approach to uncertainty quantification of groundwater models
M.S. Civil Engineering, University of Illinois at Urbana-Champaign, Aug. 2010 – May. 2012
Thesis title: Use of data-driven models to improve prediction of physically based groundwater models.
B.S. Geotechnical Engineering, Nanjing University, China, Sep. 2006 – Jun. 2010
Publications
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.
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.
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.
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.
Xu and A. J. Valocchi. Data-driven methods to improve baseflow prediction of a regional groundwater model. Computers & Geosciences, 85(B): 124-136, 2015.
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.
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.
Complete CV
CV (Last Updated September 2016)