Math Modeling is a class taught by Kristen Burns. It is unique from other math classes in that it is focused on the use of our mathematical knowledge and pushing it to our limits. The class is not focused on one particular subset of mathematics, instead putting a focus on the application of mathematical concepts through exposure to problems as opposed to lectures. There is also a lot of emphasis on group work and learning from our peers. The majority of class is spent on solving problems in small groups or as a whole class.


In Mass Academy, every student had the opportunity to participate in HiMCM. HiMCM is a 48-hour math modeling competition where teams are tasked to create a mathematical model for a real world situation, which puts an emphasis on group work and technical writing. I worked on HiMCM with Donovan Sappet, Marlon Jost, and Nathan Lam. We chose "The Need for Bees (and not just for honey)", which was a problem about modeling the changes of population in a colony of honeybees. In order to successfully model this problem, we needed to get an expansive understanding of how a honeybee population functioned, which required a lot of research. Our approach was mainly dependent on splitting the honeybee colony populations into subpopulations based on the life cycle of a bee and tracking the general changes of those subpopulations. This contest was a unique challenge due to the time limit, research process, writing, and teamwork required to be successful.


Earlier in the year, I worked with Mihika Chalasani, Anyee Li, and Amy Chen to try to find a reliable method for finding the day of the week someone was born. The method that we came up with was reliant on the fact that a normal year of 365 days consists of 52 weeks and 1 day, meaning that the day of the week of someone's birthday shifts by one day each year. A challenging part of this project was finding a consistent method that allowed the incorporation of leap years and transferring all of our findings into a mathematical representation that worked consistently.

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