Tony Wong
Assistant Professor, Applied Mathematics
Tony Wong
Assistant Professor, Applied Mathematics
Education
BS, Ohio Wesleyan University; Ph.D. University of Colorado, Boulder
Bio
My research develops mathematical and statistical methods for understanding complex systems under uncertainty. I work at the intersection of applied mathematics, statistical modeling, uncertainty quantification, scientific computing, and decision analysis. Much of my work is motivated by climate science, where computational models must be calibrated to observations, their uncertainties rigorously characterized, and their projections interpreted to support robust decision-making. More recently, I have begun extending these ideas to questions surrounding computational reasoning, artificial intelligence, and STEM education.
Uncertainty Quantification/Characterization and Climate Modeling
My primary research focuses on the mathematical and statistical foundations of probabilistic climate modeling. I develop and analyze mathematical approaches for reduced-complexity Earth system modeling, methods for model-data fusion to calibrate those models, and approaches for propagating and characterizing uncertainty in long-term climate projections.
A central application is sea-level rise and coastal hazards, where uncertainties in future climate forcing, model structure, observations, and physical processes all influence projections and the decisions based upon them. My research examines Bayesian calibration, sensitivity analysis, uncertainty quantification, and probabilistic projection methods, with the goal of producing transparent and reproducible models that support climate-risk assessment and adaptation planning. A critical line underpinning these projects is to develop open-source scientific tools to facilitate reproducible climate modeling and quantitative analysis.
Computational Literacy and STEM Education
A second area of my research examines how students and scientists develop and use computational ways of thinking. I am interested in how programming, data science, artificial intelligence, and quantitative modeling influence scientific reasoning, and how these tools can be incorporated into undergraduate STEM education.
My education research combines educational data science with quantitative and qualitative methods to investigate student learning, persistence, computational literacy, and the development of professional practices in mathematics, statistics, and computational science. An emerging direction of this work examines how artificial intelligence is changing computational research and what this means for preparing future scientists and quantitative professionals.
Learning Assistant Program
Given my interest in student learning, retention, and persistence, naturally, I got involved in the Learning Assistant Program at RIT. I currently serve as the program director, and I am always happy to chat with students and faculty alike about the many benefits of integrating LAs and active learning in STEM classes.