Wampus.fyi is an innovative web application designed to assist UT Austin students in navigating the complex task of finding suitable housing in the West Campus area. This platform enables students to anonymously share their rental experiences, costs, and insights, thus aiding their peers in making better-informed rental decisions. Additionally, the application leverages machine learning algorithms to identify high-value apartments that align with users' living goals and requirements. This approach simplifies and enhances students’ search for an optimal apartment rental.

This project presents a robust machine learning framework to address bias in medical predictive modeling, utilizing the UCI Dataset and the 2019 PhysioNet Dataset. Aimed at enhancing the predictive accuracy of sepsis outcomes, it employs a multi-model approach. Initially, six stacked machine learning models—Random Forest, Stacked Model (RF + AdaBoost + MLP), XGBoost + Logistic Regression, GBM + KNN + Decision Tree, MLP, and Deep Neural Network—are trained on preprocessed data, ensuring a comprehensive analysis of vital signs, laboratory values, and demographic information.

GSTAgri's project aims to address challenges in efficient agriculture asset management by developing an on-premises device utilizing edge computing and AI. In collaboration with GlobalStar Inc., the system focuses on monitoring crop risks, specifically predicting drought indices using metrics from Arduino sensors. The agricultural industry's need for informed decisions based on key metrics drives this initiative. The project involves capturing Bluetooth sensor data, processing it through edge devices with predictive algorithms, and presenting concise outcomes.