• M.S. University of Amsterdam, The Netherlands, 1999, Cum Laude.
• Ph.D. University of Amsterdam, The Netherlands, 2004, Cum Laude.

• Professional Training:
  • Graduate Student, University of California, Davis, 1999.
  • Graduate Student, University of Arizona, Tucson, 2001 - 2002.
  • Graduate Student, University of California, Davis, 2003.
  • Director's Funded Postdoctoral Fellow, Los Alamos National Laboratory, Los Alamos, 2005 - 2006.
  • J. Robert Oppenheimer Distinguished Postdoctoral Fellow, Los Alamos National Laboratory, Los Alamos, 2006 - present.

Research Interests:

• I am interested in developing and applying optimization and uncertainty analysis methods using evolutionary principles, Bayesian statistics, sequential data assimilation, and high performance computing to explore complex model parameter and state spaces. I particularly design methods to better analyze the mismatch between model predictions and observations (error residuals) for structural model errors, with the ultimate goal to improve our theory and understanding of the functioning of environmental systems. Most of my work is within the context of hydrology (surface and subsurface), soil physics and applied mathematics, but applications span the general field of earth and environmental sciences, including (but not limited to) ecology, hydrometeorology, weather forecasting, soil and surface water chemistry.

Selected Recent Publications:

1. J.A. Vrugt, and B.A. Robinson: Treatment of uncertainty using ensemble methods: Comparison of sequential data assimilation and Bayesian model averaging. Water Resources Research, 43, W01411, doi:10.1029/2005WR004838, 2007.
2. J.A. Vrugt, and B.A. Robinson: Improved evolutionary optimization from genetically adaptive multimethod search. Proceedings of the National Academy of Sciences of the United States of America, 104, 708 - 711, doi:10.1073/pnas.0610471104, 2007.
3. J.A. Vrugt, J. van Belle, and W. Bouten: Pareto front analysis of flight time and energy use in long distance bird migration. Journal of Avian Biology, 38, 432 - 442, doi:10.1111/j.2007.0908-8857.03909, 2007.
4. J. Koller, Y. Chen, G. D. Reeves, R. H. W. Friedel, T. E. Cayton, and J.A. Vrugt: Identifying the radiation belt source region by data assimilation. Journal of Geophysical Research - Space Physics, 112, A06244, doi:10.1029/2006JA012196, 2007.
5. J.A. Vrugt: Comment on How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration? Hydrology and Earth System Sciences, 11, 1435 - 1436, 2007.
6. P. Tittonell, M.T. van Wijk, M.C. Rufino, J.A. Vrugt, and K.E. Giller: Analyzing trade-offs in resource and labor allocation by smallholder African farmers using inverse modeling techniques. Agricultural Systems, 95, 76-95, doi:10.1016/j.agsy.2007.04.002, 2007.
7. T. W\"{ohling, J.A. Vrugt, and G.F. Barkle: Comparison of three multiobjective optimization algorithms for inverse modeling of vadose zone hydraulic properties. Soil Science Society of America Journal, 72, 305-319, doi:10.2136/sssaj2007.0176, 2008.
8. R.S. Blasone, J.A. Vrugt, H. Madsen, D. Rosbjerg, G.A. Zyvoloski, and B.A. Robinson: Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling. Advances in Water Resources, 31, 630-648, doi:10.1016/j.advwatres.2007.12.003, 2008.
9. L. Feyen, M. Khalas, and J.A. Vrugt: Semi-distributed parameter optimization and uncertainty assessment for large-scale streamflow simulation using global optimization. Hydrological Sciences Journal, 53(2), 293-208, 2008.
10. Harp, D.R., Z. Dai, A.V. Wolfsberg, J.A. Vrugt, B.A. Robinson, and V.V. Vesselinov: Aquifer structure identification using stochastic inversion. Geophysical Research Letters, 35, L08404, doi:10.1029/2008GL033585, 2008.
11. J.A. Vrugt, P.H. Stauffer, T. Wöhling, B.A. Robinson, and V.V. Vesselinov: Inverse modeling of subsurface flow and transport properties: A review with new developments. Vadose Zone Journal, 7(2), 843-864, doi:10.2136/vzj2007.0078, 2008.
12. M.P. Clark, A.G. Slater, D.E. Rupp, R.A. Woods, J.A. Vrugt, H. Gupta, T. Wagener, and L. Hay: Framework for understanding structural errors (FUSE): A modular framework to diagnose differences between hydrological models. Water Resources Research, 44, W00B02, doi:10.1029/2007WR006735, 2008.
13. H. Vereecken, J.A. Huisman, H. Bogena, J. Vanderborght, J.A. Vrugt, and J.W. Hopmans: On the value of soil moisture measurements in vadose zone hydrology: A review. Water Resources Research, 44, W00D06, doi:10.1029/2008WR006829, 2008.
14. J.A. Vrugt, B.A. Robinson, and J.M. Hyman: Self-adaptive multimethod search for global optimization in real parameter spaces. IEEE Transactions on Evolutionary Computation, 1-17, 10.1109/TEVC.2008.924428, 2008, In Press.
15. J.A. Vrugt, C.J.F. ter Baak, H.V. Gupta, and B.A. Robinson: Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling? Stochastic Environmental Research and Risk Assessment, 1-16, doi:10.1007/s00477-008-0274-y, 2008, In Press.
16. J.A. Vrugt, C.J.F. ter Braak, H.V. Gupta, and B.A. Robinson: Reply to comment by Keith Beven on Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling? Stochastic Environmental Research and Risk Assessment, 1-3, doi:10.1007/s00477-008-0284-9, 2008, In Press.
17. C.J.F. ter Braak, and J.A. Vrugt, Differential evolution Markov chain with snooker updater and fewer chains. Statistics and Computing, 1-11, doi:10.1007/s11222-008-9104-9, 2008, In Press.
18. J.A. Vrugt, C.G.H. Diks, and M.P. Clark: Ensemble Bayesian model averaging using Markov chain Monte Carlo sampling. Environmental Fluid Mechanics, 1-17, doi:10.1007/s10652-008-9106-3, 2008, In Press.
19. P.H. Stauffer, J.A. Vrugt, H.J. Turin, C.W. Gable, and W.E. Soll: Untangling diffusion from advection in unsaturated porous media: Experimental data, modeling and parameter uncertainty assessment. Vadose Zone Journal, 2008, In Press.
20. J.A. Vrugt, C.J.F. ter Braak, M.P. Clark, J.M. Hyman, and B.A. Robinson: Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation. Water Resources Research, 2008, In Press.
21. T. Wöhling, and J.A. Vrugt: Combining multi-objective optimization and Bayesian model averaging to calibrate forecast ensembles of soil hydraulic models. Water Resources Research, 2008, In Press.
22. A. Behrangi, B. Khakbaz, J.A. Vrugt, Q. Duan, and S. Sorooshian: Comment on Dynamically dimensioned search algorithm for computationally efficient watershed model calibration, by Bryan A. Tolson and Christine A. Shoemaker. Water Resources Research, 2008, In Press.
23. J.A. Vrugt, C.J.F. ter Braak, C.G.H. Diks, D. Higdon, B.A. Robinson, and J.M. Hyman: Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling. International Journal of Nonlinear Sciences and Numerical Simulation, 2008, In Press.