In Math Modeling class, we learn to tackle problems with mathematical thinking. We focus on understanding how formulas work and are derived, before memorizing them. We are currently learning about modular arithmetic and (in)finite series.
MTFC, or Modeling the Future Challenge, is a math modeling competition that attempts to teach students about actuarial science. Students analyze a topic assigned to them, as well as a separate topic of their choice, and model the scenario to determine what risks are involved, and how to mitigate them.
My team and I analyzed the potential climate impacts of AI usage, specifically, how large data centers used by AI contribute to carbon dioxide emissions and energy consumption.
Through this project, I learned how mathematical models can be used to simplify complex real-world systems while still preserving their key behaviors. We made assumptions to manage uncertainty and justified them using real data sources. This experience emphasized the importance of sensitivity analysis and understanding how small changes in inputs can significantly affect outcomes. MTFC also strengthened my ability to communicate mathematical results clearly to a non-technical audience.
For this project, groups were given the option to choose one of two scenarios and using information from the chosen scenario to create a mathematical model which provides some sort of result.
My group chose the rollercoaster scenario. Essentially, we were tasked with creating an objective ranking of rollercoasters using data such as highest drop, number of inversions, etc.
This project required us to balance mathematical rigor with practical decision-making. We explored how to normalize different metrics so that no single variable dominated the final ranking. The process highlighted trade-offs between thrill, safety, and engineering constraints. Working collaboratively also taught me how mathematical modeling benefits from multiple perspectives and iterative refinement.