Barrett

Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.

Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.

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Modeling the Complexity of Sankey Diagrams

Description

In this Barrett Honors Thesis, I developed a model to quantify the complexity of Sankey diagrams, which are a type of visualization technique that shows flow between groups. To do this, I created a carefully controlled dataset of synthetic Sankey diagrams of varying sizes as study stimuli. Then, a pair…

In this Barrett Honors Thesis, I developed a model to quantify the complexity of Sankey diagrams, which are a type of visualization technique that shows flow between groups. To do this, I created a carefully controlled dataset of synthetic Sankey diagrams of varying sizes as study stimuli. Then, a pair of online crowdsourced user studies were conducted and analyzed. User performance for Sankey diagrams of varying size and features (number of groups, number of timesteps, and number of flow crossings) were algorithmically modeled as a formula to quantify the complexity of these diagrams. Model accuracy was measured based on the performance of users in the second crowdsourced study. The results of my experiment conclusively demonstrates that the algorithmic complexity formula I created closely models the visual complexity of the Sankey Diagrams in the dataset.
ContributorsGinjpalli, Shashank (Author) / Bryan, Chris (Thesis director) / Hsiao, Sharon (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05

The Efficacy of Different Timesteps in Data when Predicting Cryptocurrency Prices

Description
This thesis serves as an experimental investigation into the potential of machine learning through attempting to predict the future price of a cryptocurrency. Through the use of web scraping, short interval data was collected on both Bitcoin and Dogecoin. Dogecoin was the dataset that was eventually used in this thesis…

This thesis serves as an experimental investigation into the potential of machine learning through attempting to predict the future price of a cryptocurrency. Through the use of web scraping, short interval data was collected on both Bitcoin and Dogecoin. Dogecoin was the dataset that was eventually used in this thesis due to its relative stability compared to Bitcoin. At the time of the data collection, Bitcoin became a much more frequent topic in the media and had more significant fluctuations due to it. The data was processed into consistent three separate, consistent timesteps, and used to generate predictive models. The models were able to accurately predict test data given all the preceding test data but were unable to autoregressively predict future data given only the first set of test data points. Ultimately, this project helps illustrate the complexities of extended future price prediction when using simple models like linear regression.
ContributorsMurwin, Andrew (Author) / Bryan, Chris (Thesis director) / Ghayekhloo, Samira (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-12
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Predicting Outcome of a Pitch Given the Type of Pitch for any Baseball Scenario

Description
This thesis serves as a baseline for the potential for prediction through machine learning (ML) in baseball. Hopefully, it also will serve as motivation for future work to expand and reach the potential of sabermetrics, advanced Statcast data and machine learning. The problem this thesis attempts to solve is predicting…

This thesis serves as a baseline for the potential for prediction through machine learning (ML) in baseball. Hopefully, it also will serve as motivation for future work to expand and reach the potential of sabermetrics, advanced Statcast data and machine learning. The problem this thesis attempts to solve is predicting the outcome of a pitch. Given proper pitch data and situational data, is it possible to predict the result or outcome of a pitch? The result or outcome refers to the specific outcome of a pitch, beyond ball or strike, but if the hitter puts the ball in play for a double, this thesis shows how I attempted to predict that type of outcome. Before diving into my methods, I take a deep look into sabermetrics, advanced statistics and the history of the two in Major League Baseball. After this, I describe my implemented machine learning experiment. First, I found a dataset that is suitable for training a pitch prediction model, I then analyzed the features and used some feature engineering to select a set of 16 features, and finally, I trained and tested a pair of ML models on the data. I used a decision tree classifier and random forest classifier to test the data. I attempted to us a long short-term memory to improve my score, but came up short. Each classifier performed at around 60% accuracy. I also experimented using a neural network approach with a long short-term memory (LSTM) model, but this approach requires more feature engineering to beat the simpler classifiers. In this thesis, I show examples of five hitters that I test the models on and the accuracy for each hitter. This work shows promise that advanced classification models (likely requiring more feature engineering) can provide even better prediction outcomes, perhaps with 70% accuracy or higher! There is much potential for future work and to improve on this thesis, mainly through the proper construction of a neural network, more in-depth feature analysis/selection/extraction, and data visualization.
ContributorsGoodman, Avi (Author) / Bryan, Chris (Thesis director) / Hsiao, Sharon (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05

A Computational Analysis on the Efficacy of Independent Redistricting Commissions

Description
Gerrymandering involves the purposeful manipulation of districts in order to gain some political advantage. Because legislators have a vested interest in continuing their tenure, they can easily hijack the redistricting process each decade for their and their political party's benefit. This threatens the cornerstone of democracy: a voter’s capability to…

Gerrymandering involves the purposeful manipulation of districts in order to gain some political advantage. Because legislators have a vested interest in continuing their tenure, they can easily hijack the redistricting process each decade for their and their political party's benefit. This threatens the cornerstone of democracy: a voter’s capability to select an elected official that accurately represents their interests. Instead, gerrymandering has legislators to choose their voters. In recent years, the Supreme Court has heard challenges to state legislature-drawn districts, most recently in Allen v. Milligan for Alabama and Moore v. Harper for North Carolina. The highest court of the United States ruled that the two state maps were gerrymandered, and in coming to their decision, the 9 justices relied on a plethora of amicus briefs- one of which included the Markov Chain Monte Carlo method, a computational method used to find gerrymandering. Because of how widespread gerrymandering has become on both sides of the political aisle, states have moved to create independent redistricting commissions. Qualitative research regarding the efficacy of independent commissions is present, but there is little research using the quantitative computational methods from these SCOTUS cases. As a result, my thesis will use the Markov Chain Monte Carlo method to answer if impartial redistricting commissions (like we have in Arizona) actually preclude unfair redistricting practices. My completed project is located here: https://dheetideliwala.github.io/honors-thesis/
ContributorsDeliwala, Dheeti (Author) / Bryan, Chris (Thesis director) / Strickland, James (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Politics and Global Studies (Contributor)
Created2023-12

deliwala_fall23_ppt.pdf

ContributorsDeliwala, Dheeti (Author) / Bryan, Chris (Thesis director) / Strickland, James (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Politics and Global Studies (Contributor)
Created2023-12

deliwala_fall23.pdf

ContributorsDeliwala, Dheeti (Author) / Bryan, Chris (Thesis director) / Strickland, James (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Politics and Global Studies (Contributor)
Created2023-12