Tony Wong Headshot

Tony Wong

Assistant Professor, Applied Mathematics

School of Mathematics and Statistics
College of Science
aewsma@rit.edu
585-475-7486
Office Location
GOS-2232

Tony Wong

Assistant Professor, Applied Mathematics

School of Mathematics and Statistics
College of Science

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.

aewsma@rit.edu
585-475-7486
Personal Links
Personal Website
ORCID
Curriculum Vitae
LinkedIn
Areas of Expertise
Model Calibration
Uncertainty Characterization
Bayesian Statistics
Geophysical Modeling
Climate Change
Sea-level Rise
Coastal Impacts
Mathematical Modeling

Select Scholarship

Journal Paper
Kaylor, Sydney, Yasin Elshorbany, and Tony Wong. "Economic Cost of No-Adaptation and Least-Cost Cases of Sea Level Rise in the U.S." Environmental Research Communications. (2026): 1-32. Web.
Estevez, Carolina, et al. "Regret-based global coastal adaptation decision-making under sea level uncertainty." PLOS Climate 5. 8 (2026): 1-21. Web.
Childs, Meghan R and Tony E Wong. "Enhancing a university community COVID-19 model with Bayesian model-data fusion." Mathematics in Medical and Life Sciences 2. 1 (2025): 1-15. Web.
Wong, Tony, et al. "Coastal adaptation and damage costs at different global warming thresholds." npj Natural Hazards 2. 35 (2025): 1-9. Web.
Darnell, Chloe, et al. "The interplay of future emissions and geophysical uncertainties for projections of sea-level rise." Nature Climate Change. (2025): 1-13. Web.
Ramme, Lennart, et al. "Feedback-based sea level rise impact modelling for integrated assessment models with FRISIAv1.0." Geoscientific Model Development 18. (2025): 10017–10052. Web.
Bundy, Cameron and Tony Wong. "Analyzing Learning Assistant influence on STEM student success using logistic and hierarchical regression." Contemporary Mathematics and Science Education 6. 1 (2025): 1-10. Web.
Childs, Meghan Rowan and Tony E Wong. "Assessing parameter sensitivity in a university campus COVID-19 model with vaccinations." Infectious Disease Modelling 8. 2 (2023): 374-389. Web.
Tedeschi, Mason N., et al. "Improving models for student retention and graduation using Markov chains." PLoS ONE 18. 6 (2023): 1-14. Web.
Wong, Tony E., et al. "Evidence for Increasing Frequency of Extreme Coastal Sea Levels." Frontiers in Climate. (2022): 1-12. Web.
Hough, Alana and Tony E Wong. "Analysis of the Evolution of Parametric Drivers of High-End Sea-Level Hazards." Advances in Statistical Climatology, Meteorology and Oceanography. (2022): 117–134. Web.
Wong, Tony E., et al. "MimiBRICK.jl: A Julia package for the BRICK model for sea-level change in the Mimi integrated modeling framework." Journal of Open Source Software 7. 76 (2022): 4556. Web.
Srikrishnan, Vivek, et al. "Uncertainty analysis in multi-sector systems: Considerations for risk analysis, projection, and planning for complex systems." Earth’s Future 10. (2022): 15. Web.
Rennert, Kevin, et al. "Comprehensive Evidence Implies a Higher Social Cost of CO2." Nature. (2022): 1-42. Web.
Wong, Tony E., et al. "Sea Level and Socioeconomic Uncertainty Drives High-End Coastal Adaptation Costs." Earth's Future 10. (2022): e2022EF003061. Web.
Wong, Tony E, et al. "Evaluating the Sensitivity of SARS-CoV-2 Infection Rates on College Campuses to Wastewater Surveillance." Infectious Disease Modelling 6. (2021): 1144-1158. Web.
Wong, Tony E, et al. "A Tighter Constraint on Earth-System Sensitivity from Long-Term Temperature and Carbon-Cycle Observations." Nature Communications 12. (2021): 1-8. Web.
Vega‐Westhoff, Ben, et al. "Impacts of Observational Constraints Related to Sea Level on Estimates of Climate Sensitivity." Earth's Future 7. 6 (2019): 677-690. Web.
Brady, E., et al. "The Connected Isotopic Water Cycle in the Community Earth System Model Version 1." Journal of Advances in Modeling Earth Systems 11. 8 (2019): 2547-2566. Web.
Published Conference Proceedings
Foster, Michael, et al. "Toward an Assessment of Students’ (Social) Computational Literacy." Proceedings of the 17th International Conference on Computer-Supported Collaborative Learning - CSCL 2024. Ed. J. Clarke-Midura, et al. Buffalo, NY: International Society of the Learning Sciences, 2024. Web.
Invited Article/Publication
Wong, Tony E. "If Everyone on Earth Sat in the Ocean at Once, How Much Would Sea Level Rise?" The Conversation. (2021). Web.
Wong, Tony E. "Lasting Coastal Hazards from Past Greenhouse Gas Emissions." Proceedings of the National Academy of Sciences. (2019). Web.

Currently Teaching

MATH-495
Undergraduate Research in Mathematical Sciences
1-3 Credits
This course is a faculty-directed project that could be considered original in nature. The level of work is appropriate for students in their final two years of undergraduate study.
MATH-505
Stochastic Processes
3 Credits
This course explores Poisson processes and Markov chains with an emphasis on applications. Extensive use is made of conditional probability and conditional expectation. Further topics, such as renewal processes, Brownian motion, queuing models and reliability are discussed as time allows.
MATH-605
Stochastic Processes
3 Credits
This course is an introduction to stochastic processes and their various applications. It covers the development of basic properties and applications of Poisson processes and Markov chains in discrete and continuous time. Extensive use is made of conditional probability and conditional expectation. Further topics such as renewal processes, reliability and Brownian motion may be discussed as time allows.
MATH-790
Research & Thesis
0-9 Credits
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.
MATH-791
Continuation of Thesis
1 Credit
Continuation of Thesis
STAT-747
Principles of Statistical Data Mining
3 Credits
This course covers topics such as clustering, classification and regression trees, multiple linear regression under various conditions, logistic regression, PCA and kernel PCA, model-based clustering via mixture of gaussians, spectral clustering, text mining, neural networks, support vector machines, multidimensional scaling, variable selection, model selection, k-means clustering, k-nearest neighbors classifiers, statistical tools for modern machine learning and data mining, naïve Bayes classifiers, variance reduction methods (bagging) and ensemble methods for predictive optimality.

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