The incidence of childhood obesity has become increasingly prevalent in the United States in recent years. The development of obesity at any age, but especially in adolescence, can have lasting negative effects in the form of cardiometabolic disease, increased incurred healthcare costs, and potential negative effects on quality of life. In recent years, a rising trend of obesity, in both adults and adolescents, has been observed in lower income and ethnic groups. Increased adiposity can be influenced by modifiable factors -(physical activity, caloric intake, or sleep) or by non-modifiable factors (ethnicity, genetic predispositions, and socioeconomic status). The influence of these factors can be observed in individuals of all ages, including infants. A common indicator of the development of childhood obesity is rapid weight gain (RWG) within an infant’s first year of life. The composition of the gut microbiome can act as a predictor for RWG and the development of childhood obesity. Infants are exposed to an immense microbial load when they are born and their gut microbiome is continually diversified through their method of feeding and the subsequent introduction to solid foods. While currently understudied, it is understood that cultural and socioeconomic factors influence the development of the gut microbiome, which is further explored in this analysis. The DNA from 51 fecal samples from infants ranging from 3 weeks to 12 months in age was extracted and sequenced using next-generation sequencing, and the resulting sequences were analyzed using QIIME 2. Results from alpha-diversity and beta-diversity metrics showed significant differences in the gut microbiome of infants when comparing groups based on baby race/ethnicity, household income, and mom’s education. These findings suggest the importance of sociodemographic characteristics in shaping the gut microbiome and suggest the importance of future studies including diverse populations in gut microbiome work.

Regulation in the insurance market has increased greatly over the past four decades, and recent regulatory frameworks such as Solvency II have made simulations increasingly important. Monte Carlo simulations are often too inefficient to be used by themselves, and these Monte Carlo simulations begin to struggle when the complexity of insurance contracts increases. For that reason, there have been numerous suggested improvements to traditional MC methods such as the sample recycling method and a neural network method. This thesis will review various risk measures, the methods used to calculate them, and a detailed analysis of the neural network method and the sample recycling method. The sample recycling method and the neural network method will then be analyzed in detail, and a comparative analysis of the sample recycling method and the neural network method will be given. It was discovered that both the sample recycling method and the neural network method provide a large improvement in computational cost and overall run time with minor impacts on the accuracy. Thus, it was concluded that the sample recycling method is best suited for contracts where the inner loop estimations are particularly complex and the neural network is a general method that pairs well with complex input portfolios.

Mink Social is a startup aimed at providing podcasters with a suite of tools to help them edit and market their content on social media. The company aims to solve the niche problem of the difficulties podcasters face in promoting their long-form audio content online. Mink Social's unique solution involves automating the backend work of editing and repurposing content into easy-to-market clips. The company's business model targets three distinct market segments: entry-level podcasters, established podcasters, and enterprise podcasting. Mink Social's competitive advantage is that it is the first social media promotion and editing platform exclusively for podcasts.

Our Founders Lab group created a pop-up thrift store on campus using donated clothing. We generated over $1000 in profits from our clothing sales to donate to a local charity, making our team the highest earning Founders Lab team. Our shop, Devils Rack, tabled twice on the Tempe ASU campus in order to generate our revenue.

Students for Success is a consulting firm designed to connect undergraduate ASU students with those applying to ASU. Our counselors are well versed in scholarships, housing, programs, clubs, and deadline information. This firm provides flexible work for undergraduate students, while giving applicants a custom experience from a student who truly understands the most recent application process.

“Social Sports is an application which facilitates the environment fans need to support their teams, in doing so our application aids hospitality businesses market their events and brings business during their downtime. Social Sports allows businesses to market their sports screening events to fans and supporters. Fans and supporters using Social Sports are able to see the percentage of supporters/fans on each side and decide which bar or restaurant to go watch the game. Social Sport’s mission is to connect sports fans with other like minded passionate fans and enable community formation and allow sports fans around the world to socialize with much ease.”

In the U.S., the annual NCAA college basketball tournament, known as March Madness, draws in millions of people trying to predict who will win. Just one problem: no one has ever created a perfect bracket. By using a player-based rating system that updates throughout the season, a “predictive model” can be created to accurately predict teams with the best shot of winning the championship, and even show which players had the most impact on a single team in college basketball.

This project tackles a real-world example of a classroom with college students to discover what factors affect a student’s outcome in the class as well as investigate when and why a student who started well in the semester may end poorly later on. First, this project performs a statistical analysis to ensure that the total score of a student is truly based on the factors given in the dataset instead of due to random chance. Next, factors that are the most significant in affecting the outcome of scores in zyBook assignments are discovered. Thirdly, visualization of how students perform over time is displayed for the student body as a whole and students who started well at the beginning of the semester but trailed off towards the end. Lastly, the project also gives insight into the failure metrics for good starter students who unfortunately did not perform as well later in the course.