The General Idea:
I am interested in addressing the implications of the uncertainty that is inherent in any physical model, and examining how best to constrain and characterize these uncertainties and their effects on decision-making.

More Specifically... Uncertainty in climate model projections, sea-level rise in particular, can lead to suboptimal, ineffective, and potentially dangerous policy decisions. To avoid this, we must use the information we have available make the best possible policy decisions. This requires accounting for not only varying forms of uncertainty in model parameters and projections, but deep uncertainty - uncertainty in the uncertainty in model structure and parameters. Statistical calibration approaches allow us to constrain these models and characterize the uncertainties inherent in both the model and data, and are a critical part of any modeling effort.