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.
I am interested in future projections of sea-level rise and their impacts on coastal defense and adaptation decision-making. This includes examining statistical model calibration techniques and extreme value statistical models. I am currently looking for students at all levels, and aim to create a research group with a diversity of culture, experiences and ways of thinking. If you are interested in chatting about research, potential projects or anything, feel free to shoot me an email or stop by my office.
In the News
March 29, 2021
If everyone on Earth sat in the ocean at once, how much would sea level rise?
Tony Wong, assistant professor of mathematical sciences, explains volume and displacement for the "Curious Kids" series published by The Conversation.
March 12, 2021
The power of science
Essay by Sophia Maggelakis, dean of the College of Science, published by the Rochester Beacon.