Education
  • Ph.D. in Computational Science, Florida State University, 2012
  • M. S. in Hydrology and Water Resources, China University of Geosciences, 2007
  • B. S. in Environmental Engineering, Hebei University of Geosciences, 2004
Professional Experiences
  • 2016 -- Now: Research Scientist, Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN
  • 2013 -- 2016: Postdoctoral Research Associate, Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN
  • 2012 -- 2013: Postdoctoral Research Associate, United States Geological Survey, Menlo Park, CA
  • 2007 -- 2012: Graduate Research Assistant, Department of Scientific Computing Florida State University, Tallahassee, FL
Research Interest
  • Uncertainty quantification and risk assessment of hydrologic and climate models
  • Numerical simulation of groundwater flow and contaminant reactive transport
  • Numerical methods and algorithms for inverse modeling
  • Hierarchical Bayesian inference and statistical methods
  • Data collection design and data assimilation

Curriculum Vitae

Journal Publications
  • M. Xi, D. Lu, D. Gui, Z. Qi and G. Zhang, Calibration of an Agricultural-Hydrological Model (RZWQM2) Using Surrogate Global Optimization, Journal of Hydrology, to appear in 2016.
  • D. Lu, G. Zhang, C. Webster, and C. Barbier, An Improved Multilevel Monte Carlo Method for Estimating Probability Distribution Functions in Stochastic Oil Reservoir Simulations, Water Resources Research, to appear in 2016.
  • P. Liu, M. Ye, P. Beerli, X. Zeng, D. Lu, and Y. Tao, Evaluate Model Probability Using Markov Chain Monte Carlo with Thermodynamics Integration, Water Resources Research, Vol. 52(2), pp. 734--758, 2016.
  • M. C. Hill, D. Kavetski, M. Clark, M. Ye, M. Arabi, D. Lu, L. Foglia, and S. Mehl, Practical Use of Computationally Frugal Model Analysis Methods, Ground Water, Vol. 54(2), pp. 59--170, 2016.
  • D. Lu, M. Ye, and G. P. Curtis, Maximum Likelihood Bayesian Model Averaging and Its Predictive Analysis for Groundwater Reactive Transport Models, Journal of Hydrology, Vol. 529(3), pp. 1859–1873, 2015.
  • D. Lu, M. Ye, M. C. Hill, E. P. Poeter, and G. P. Curtis, A Computer Program for Uncertainty Analysis Integrating Regression and Bayesian Methods, Environmental Modeling & Software, Vol. 60, pp. 41–56, 2014.
  • G. Zhang, D. Lu, M. Ye, M. Gunzburger, and C. Webster, An Adaptive Sparse- Grid High-Order Stochastic Collocation Method of Bayesian Inference in Groundwater Reactive Transport Modeling, Water Resources Research, Vol. 49(10), pp. 6871– 6892, 2013.
  • D. Lu, M. Ye, P. D. Meyer, G. P. Curtis, X. Shi, X. Niu, and S. B. Yabusaki, Effects of Error Covariance Structure on Estimation of Model Averaging Weights and Predictive Performance, Water Resources Research, Vol. 49(9), pp. 6029–6047, 2013.
  • M. C. Hill, D. Kavetski, M. Clark, M. Ye, and D. Lu, Uncertainty Quantification for Environmental Models, SIAM News, Vol.45(9), 2012.
  • D. Lu, M. C. Hill, and M. Ye, Analysis of Regression Confidence Intervals and Bayesian Credible Intervals for Uncertainty Quantification, Water Resources Research, Vol. 48(9), W09521, 2012.
    (This paper was selected as Editor’s Highlight entitled new insights into faster computation of uncertainties)
  • D. Lu, M. Ye, S. P. Neuman, and L. Xue, Multimodel Bayesian Analysis of Data-Worth Applied to Unsaturated Fractured Tuffs, Advances in Water Resources, Vol. 35, pp. 69–82, 2012.
  • S. P. Neuman, L. Xue, M. Ye, and D. Lu, Bayesian Analysis of Data-Worth Consid- ering Model and Parameter Uncertainties, Advances in Water Resources, Vol. 36, pp. 75–85, 2012.
    (Top 10 Cited Paper in 2012-2013 of Advances in Water Resources [Certificate])
  • D. Lu, M. Ye, and S. P. Neuman, Dependence of Bayesian Model Selection Criteria and Fisher Information Matrix on Sample Size, Mathematical Geoscience, Vol. 43, pp. 971–993, 2011.
  • M. Ye, D. Lu, S. P. Neuman, and P. D. Meyer, Comment on ”Inverse groundwater modeling for hydraulic conductivity estimation using Bayesian model averaging and variance window” by Frank T.-C. Tsai and Xiaobao Li, Water Resources Research, Vol. 46, W02801, 2010.
Papers in Progress
  • S. Mo, D. Lu, X. Shi, J. Wu, and J. Wu, An Adaptive Sampling Strategy For Data Interpolation Using Radial Basis Functions, Water Resources Research, submitted.
  • D. Lu, M. Stoyanov, G. Zhang, and C. Webster, TASMANIAN: A Software for High-Dimensional Surrogate Modeling in Uncertainty Quantification, Environmental Modeling & Software, in preparation.
  • D. Lu, and D. Ricciuto, A Bayesian Calibration of a Ecosystem Carbon Model: A Comparative Study of Markov Chain Monte Carlo Methods, in preparation.
Referred Conference Publications
  • G. Zhang, D. Lu, M. Ye, M. Gunzburger, and C. Webster, An Efficient Surrogate Modeling Approach in Bayesian Uncertainty Analysis, AIP Conference Proceedings, Vol. 1558, pp. 898–901, 2013.
  • M. Ye, D. Lu, S. P. Neuman, and L. Xue , Multimodel Bayesian Analysis of Data- Worth Applied to Unsaturated Fractured Tuffs, International Conference on Groundwater: Our Source of Security in an Uncertain Future, Pretoria, South Africa, 2011.
  • D. Lu, M. C. Hill, and M. Ye, Analysis of Regression and Bayesian Predictive Uncertainty Measures, MODFLOW and More 2011 Conference, Golden, CO, 2011.
  • M. Ye, D. Lu, G. Miller, G. P. Curtis, P. D. Meyer, and S. B. Yabusaki, Assessment of Predictive Uncertainty in Coupled Groundwater Reactive Transport Modeling, Conference on Goldschmidt 2010-Earth, Energy and the Environment, Knoxville, TN, 2010.
Technical Reports
  • C. Barbier, D. Lu, N. Collier, F. Curtis, C. Webster, and Y. Polsky, High Perfor- mance Computing Simulations for Shale Gas Formation Flow Transport and Uncertainty Quantification Analysis, ORNL Technical Report, ORNL/TM-2015/543, 2015.

Software Development
  • UCODE 2014: A Computer Code for Universal Inverse Modeling
    • Sponsor: U.S. Geological Survey
    • Developers: Eileen P. Poeter, Mary C. Hill, Dan Lu, and Steffen Mehl
    • Webpage: http//:􏰀igwmc.mines.edu/freeware/ucode
    • Description: UCODE is one of a set of inverse modeling codes supported by the U.S. Geological Survey.
      UCODE was developed for models in which the number of parameters is less than the number of observations. It can be used with existing process models to perform sensitivity analysis, data needs assessment, model calibration, prediction and uncertainty quantification. 

Oral Presentations
  • A Systematic Bayesian Framework for Uncertainty Quantification in Environmental Modeling, Earth System Modeling Workshop, Oak Ridge National Laboratory, TN, 2015.
  • Multilevel Monte Carlo Method with Application to Uncertainty Quantification in Oil Reservoir Simulation, 48th American Geophysics Union Annual Meeting, San Francisco, CA, 2014.
  • Assessment of Predictive Performance of Bayesian Model Averaging in Groundwater Reactive Transport Models, 2014 SIAM Conference on Uncertainty Quantification, Savannah, GA, 2014.
  • Maximum Likelihood Bayesian Model Averaging of Groundwater Reactive Transport Models, 2014 SIAM SEAS Annual Meeting, Melbourne, FL, 2014.
  • Integration of Markov Chain Monte Carlo Simulation into UCODE for Bayesian Uncertainty Analysis, Geological Society of America Annual Meeting, Charlotte, NC, 2012.
  • Effects of Temporal Error Correlation on Quantification of Predictive Uncertainty in Groundwater Reactive Transport Modeling, annual PI meeting of the Subsurface Biogeochemical Research Program of the Department of Energy, Washington D.C., 2012.
  • Effects of Temporal Residual Correlation on Model Weights, 44th American Geophysics Union Annual Meeting, San Francisco, CA, 2011.
  • Multimodel Bayesian Analysis of Data-Worth Applied to Unsaturated Fractured Tuffs, Geosciences Applications Opening Workshops on Uncertainty Quantification, Research Triangle Park, NC, 2011.
  • Analysis of Predictive Uncertainty Measures of Regression and Bayesian, 2011 MODFLOW and More Meeting, Golden, CO, 2011.
  • A Controlled Experiment for Investigating Prediction Accuracy and Prediction Uncertainty in Groundwater Flow Modeling, 43th American Geophysics Union Annual Meeting, San Francisco, CA, 2010.