Barrett, The Honors College Thesis/Creative Project Collection

The focus of my honors thesis is to find ways to use deep learning in tandem with tools in statistical mechanics to derive new ways to solve problems in biophysics. More specifically, I’ve been interested in finding transition pathways between two known states of a biomolecule. This is because understanding the mechanisms in which proteins fold and ligands bind is crucial to creating new medicines and understanding biological processes. In this thesis, I work with individuals in the Singharoy lab to develop a formulation to utilize reinforcement learning and sampling-based robotics planning to derive low free energy transition pathways between two known states. Our formulation uses Jarzynski’s equality and the stiff-spring approximation to obtain point estimates of energy, and construct an informed path search with atomistic resolution. At the core of this framework, is our first ever attempt we use a policy driven adaptive steered molecular dynamics (SMD) to control our molecular dynamics simulations. We show that both the reinforcement learning (RL) and robotics planning realization of the RL-guided framework can solve for pathways on toy analytical surfaces and alanine dipeptide.

For my Honors Thesis, I decided to create an Artificial Intelligence Project to predict Fantasy NFL Football Points of players and team's defense. I created a Tensorflow Keras AI Regression model and created a Flask API that holds the AI model, and a Django Try-It Page for the user to use the model. These services are hosted on ASU's AWS service. In my Flask API, it actively gathers data from Pro-Football-Reference, then calculates the fantasy points. Let’s say the current year is 2022, then the model analyzes each player and trains on all data from available from 2000 to 2020 data, tests the data on 2021 data, and predicts for 2022 year. The Django Website asks the user to input the current year, then the user clicks the submit button runs the AI model, and the process explained earlier. Next, the user enters the player's name for the point prediction and the website predicts the last 5 rows with 4 being the previous fantasy points and the 5th row being the prediction.

The field of quantum computing is an exciting area of research that allows quantum mechanics such as superposition, interference, and entanglement to be utilized in solving complex computing problems. One real world application of quantum computing involves applying it to machine learning problems. In this thesis, I explore the effects of choosing different circuit ansatz and optimizers on the performance of a variational quantum classifier tasked with binary classification.

My project goes over the biology of chronic kidney disease, including what CKD is, definitions to understand, the prevalence of it, the signs and symptoms, and the main causes. Then I go over the nutrition guidelines of the disease and the nutrients important to regulate, including sodium, protein, fats and whole grain carbohydrates, potassium, and phosphorus. Next, I write about recipes that I modify to be kidney-friendly and make nutrient analyses for each original recipe and each modified recipe. Finally, I made a video of myself cooking and trying two of the modified recipes.

This thesis project focuses on the creation and assessment of the "Simple Stocks" app, a straightforward investment tool specifically developed for people who are new to investing and find it challenging to comprehend the complexities of the stock market. We identified a significant gap in the availability of easy-to-understand resources and information for beginner investors, which led us to design an app that provides clear and simple data, professional advice from financial analysts, and an advanced machine learning feature to predict stock trends. The "Simple Stocks" app also incorporates a voting feature, allowing users to see what other investors think about specific stocks. This functionality not only helps users make informed decisions but also encourages a sense of community, as users can learn from each other's experiences and opinions. By creating a supportive environment, the app promotes a more approachable and enjoyable experience for those who are new to investing. Following the successful release of the "Simple Stocks'' app on the App Store, our current objectives include expanding the user base and looking into various ways to generate income. One possible approach is to collaborate with other companies and establish an advertising-based revenue model, which would benefit both parties by attracting more users and increasing profits.

This thesis project focuses on the creation and assessment of the "Simple Stocks" app, a straightforward investment tool specifically developed for people who are new to investing and find it challenging to comprehend the complexities of the stock market. We identified a significant gap in the availability of easy-to-understand resources and information for beginner investors, which led us to design an app that provides clear and simple data, professional advice from financial analysts, and an advanced machine learning feature to predict stock trends. The "Simple Stocks" app also incorporates a voting feature, allowing users to see what other investors think about specific stocks. This functionality not only helps users make informed decisions but also encourages a sense of community, as users can learn from each other's experiences and opinions. By creating a supportive environment, the app promotes a more approachable and enjoyable experience for those who are new to investing. Following the successful release of the "Simple Stocks'' app on the App Store, our current objectives include expanding the user base and looking into various ways to generate income. One possible approach is to collaborate with other companies and establish an advertising-based revenue model, which would benefit both parties by attracting more users and increasing profits.