Projects
Predicting the Unpredictable: Machine Learning for Lead Time Forecasting in Supply Chains with Extreme Delivery Deviations
◾ Creating prediction models for the supply chain of a large manufacturing company. Utilized machine learning methods as well as deep learning to give confident predictions on part orders timeliness.
◾ Presented research poster at 2025 INFORMS Analytics+ National Conference.
Leveraging Web Scraping to Build Purdue’s Highly Prestigious Awards Historical Database
◾ Utilized web scraping methods in Python to scrape award websites of winners and their metadata. As well as implementing various APIs to check institutional connection and data standardization.
◾ Presented to Association of American Universities (AAU) in Fall 2024, at Indiana Association for Institutional Research (INAIR) 2025 Conference, and at Association for Institutional Research 2025 National Conference.
Using Machine Learning to Map Grief Stages and Optimize Support for Military Survivors
◾ Collaborated with Tragedy Assistance Program for Survivors (TAPS) to use machine learning classification models to predict grief stages based on survey responses. Achieved classification accuracy of 89%.
◾ Presented research poster at Purdue Fall 2024 Undergraduate Research Conference.