Tony E. Wong

My research is centered around the implications of the uncertainty that is inherent in any physical model, and in any data set, 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 – at worst – outright 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. My research interests include future projections of sea-level rise and their impacts on coastal defense decision-making, and identifying data scientific approaches for improving estimates of basic Earth system properties.

I received my PhD in 2016 from the University of Colorado Boulder in Applied Mathematics, where my work focused on developing a mathematical framework for fusing hydrological observational data with a state-of-the-art land surface climate model. I then held a postdoctoral scholar position with the Earth and Environmental Systems Institute at Pennsylvania State University, where my research pivoted slightly toward constraining larger-scale Earth system properties and climate projections (such as sea-level rise and global warming). After that, I spent two years teaching computer science at the University of Colorado Boulder, developing data scientific curriculum and continued my research in climate change uncertainties and impacts. In fall 2019, I joined the School of Mathematical Sciences at the Rochester Institute of Technology as an assistant professor.

I have >20 peer-reviewed journal articles, one of which is presently in the top 5% of all research outputs scored by Altmetric (DOI: 10.1002/2017ef000607), regularly serve as a referee for Earth/climate science journals (PNAS, Journal of Climate, Environmental Research Letters, for example), and served as an expert reviewer for the New York City Panel on Climate Change 2019 Report.