Tony Wong Headshot

Tony Wong

Assistant Professor

School of Mathematical Sciences
College of Science

585-475-7486
Office Hours
On Zoom: Monday 2:30-4, Thursday 11-12:30, and by appointment.
Office Location

Tony Wong

Assistant Professor

School of Mathematical Sciences
College of Science

Bio

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.

585-475-7486

Areas of Expertise

Select Scholarship

Journal Paper
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.
Invited Article/Publication
Wong, Tony E. "Lasting Coastal Hazards from Past Greenhouse Gas Emissions." Proceedings of the National Academy of Sciences. (2019). Web.

Currently Teaching

MATH-251
3 Credits
This course introduces sample spaces and events, axioms of probability, counting techniques, conditional probability and independence, distributions of discrete and continuous random variables, joint distributions (discrete and continuous), the central limit theorem, descriptive statistics, interval estimation, and applications of probability and statistics to real-world problems. A statistical package such as Minitab or R is used for data analysis and statistical applications.
MATH-790
0 - 9 Credits
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.
MATH-605
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-495
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
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-498
1 - 3 Credits
This course is a faculty-guided investigation into appropriate topics that are not part of the curriculum.

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