Barrett, The Honors College Thesis/Creative Project Collection
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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- Creators: Balasooriya, Janaka
Description
Next year, Arizona State University is launching a chatbot that will place their knowledge and services right into the palms of their students’ hands. Currently named Sunny, this virtual assistant will be able to answer questions regarding all aspects of college life, from orientation to housing, financial aid, schedules, intramurals, and more. Over the last semester, I have met with members of the Sunny development team to discuss their design and implementation plans. With their information plus a bit of outside research, I was able to combine several frameworks and technologies to build a prototype for Sunny. Prototypes allow developers to evaluate their designs early on, giving them ample time to make any necessary adjustments. I am confident that the Sunny development team will be able to learn from my basic implementation, from its triumphs and failures, to create the best possible chatbot for the students attending Arizona State University.
ContributorsGrossnickle, Brandon Michael (Co-author) / Grossnickle, Brandon (Co-author) / Balasooriya, Janaka (Thesis director) / Gray, Bobby (Committee member) / Longie, Joel (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
The aim of this project was to provide college applicants with the ability to apply using a video instead of an essay. These videos are analyzed automatically and their scripts are taken and submitted with the application. This was implemented through the use of Amazon Web Services (AWS) and their S3 buckets along with their speech to text transcription service. This type of application process can give admissions teams the opportunity to get to know who will potentially be attending their university and allows the applicants to express themselves to admissions teams in a new and unique way.
ContributorsStephan, Meagan (Co-author) / Pratt, Devan (Co-author) / Chen, Yinong (Thesis director) / Balasooriya, Janaka (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
Appointment scheduling in health care systems is a well-established domain, however, the top commercial services neglect scheduling analytics. This project explores the benefit of utilizing data analysis to equip health care offices with insights on how to improve their existing schedules. The insights are generated by comparing patients’ preferred appointment times with the current schedule coverage and calculating utilization of past appointments. While untested in the field, the project yielded promising results using generated sample data as a proof of concept for the benefits of using data analytics to remove deficiencies in a health care office’s schedule.
ContributorsBowman, Jedde James (Author) / Chen, Yinong (Thesis director) / Balasooriya, Janaka (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
My thesis is an exploration on the principles of algorithmic trading. I was introduced to the world of algorithmic trading in the Summer of 2018 when I got an internship at a startup trading firm called Helios Machine Intelligence. At HeliosMI, my job was to model algorithms for their in-house developed platform (in Java and C#). I learned how to model several different strategies, but I didn’t understand how, or more importantly, why these strategies worked. In the Spring of 2019 when I first began planning my thesis, I initially planned on recreating and optimizing HeliosMI’s trading platform. It was after reading a few books over the summer, namely; The Man Who Solved the Market by Gregory Zuckerman, Algorithmic Trading by Ernie Chan, and A Random Walk Down Wall Street by Burton Gordon Malkiel, that I realized that I was much more interested in learning the fundamentals of algorithmic trading, so I decided to make this the new focus of my thesis. At HeliosMI, we tested strategies against the historical data of stocks using an application called QuantConnect. This application is easy-to-use, cheap (even offering a free tier) and provides plenty of documentation with an active community forum, making it the obvious choice as the platform for my thesis research. Throughout my research I focused on exploring high-frequency trading algorithms, mainly because these are the types of algorithms that are employed at Wall Street hedge funds, and also the type I worked on at HeliosMI. I developed three distinct algorithms throughout my research; a momentum based strategy, a mean reversion based strategy, and a preferred time of day based strategy. In my thesis report, I go in depth on each of these strategies, as well as discuss the history of algorithmic trading, and explore some future research aspirations.
ContributorsMaheshwari, Nicholas Leo (Author) / Balasooriya, Janaka (Thesis director) / Hoffman, David (Committee member) / Dean, W.P. Carey School of Business (Contributor) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Although Spotify’s extensive library of songs are often seen broken up by “Top 100” and main lyrical genres, these categories are primarily based on popularity, artist and general mood alone. If a user wanted to create a playlist based on specific or situationally specific qualifiers from their own downloaded library, he/she would have to hand pick songs that fit the mold and create a new playlist. This is a time consuming process that may not produce the most efficient result due to human error. The objective of this project, therefore, was to develop an application to streamline this process, optimize efficiency, and fill this user need.
Song Sift is an application built using Angular that allows users to filter and sort their song library to create specific playlists using the Spotify Web API. Utilizing the audio feature data that Spotify attaches to every song in their library, users can filter their downloaded Spotify songs based on four main attributes: (1) energy (how energetic a song sounds), (2) danceability (how danceable a song is), (3) valence (how happy a song sounds), and (4) loudness (average volume of a song). Once the user has created a playlist that fits their desired genre, he/she can easily export it to their Spotify account with the click of a button.
Song Sift is an application built using Angular that allows users to filter and sort their song library to create specific playlists using the Spotify Web API. Utilizing the audio feature data that Spotify attaches to every song in their library, users can filter their downloaded Spotify songs based on four main attributes: (1) energy (how energetic a song sounds), (2) danceability (how danceable a song is), (3) valence (how happy a song sounds), and (4) loudness (average volume of a song). Once the user has created a playlist that fits their desired genre, he/she can easily export it to their Spotify account with the click of a button.
ContributorsDiMuro, Louis (Author) / Balasooriya, Janaka (Thesis director) / Chen, Yinong (Committee member) / Arts, Media and Engineering Sch T (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05