Hi! I'm Tony
I'm a professor in the School of Mathematics and Statistics at the Rochester Institute of Technology.
Mathematics is a powerful tool that enables us to answer interesting questions. In my research, I use mathematical and statistical tools to characterize uncertainty in physical models. I am particularly interested in assessing how these modeling uncertainties map to uncertainty in decision-making, in settings such as mitigating dangerous climate risks. In the classroom, I like to draw on these experiences to (hopefully!) show students that mathematics is much more than just a set of prerequisites for their other coursework. It turns out that mathematics and statistics are the foundation of... well, pretty much all of science.
Office hours, Spring 2024: by appointment
If you need to meet and chat outside of regularly scheduled office hours, send me an email to set something up so we can have a focused conversation.
Uncertainty in climate model projections, sea level rise in particular, can lead to suboptimal and ineffective policy decisions. Using the data we have available to make good decisions generally requires accounting for not only varying forms of uncertainty in model parameters and projections, but also deep uncertainties like uncertainty in model structure and forcing. 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 particularly interested in future projections of sea-level rise and their impacts on coastal defense decision-making. This includes examining statistical model calibration techniques and extreme value statistical models.
I am also interested in educational data analytics and efforts to assess, promote, and enhance computational literacy. For example, I'm interested in both leveraging educational data in new and interesting ways to assess outcomes like student learning, retention and persistence, as well as using new and interesting educational data to assess these outcomes. These projects often involve a heavy dose of computation, which ties into my interest in computational literacy. Broadly speaking, this can describe how we use computation as a way to approach and solve problems, as well as communicate scientific/scholarly information within and across disciplines.
Pictures speak a thousand words, so here's a picture made up of words from my Google Scholar page.