PhD, Operations Research and Industrial Engineering, Aug 2025
The University of Texas at Austin
Masters, Operations Research and Industrial Engineering, Dec 2023
The University of Texas at Austin
Bachelor of Science, Mechanical Engineering, May 2018
The University of Texas at Austin
Machine Learning Lab Intern, Samsung Advanced Institute of Technology (SAIT)
October 2022 – January 2023
Analyzed Optical Emission Spectroscopy recordings from plasma etch tools for use in Virtual Metrology (VM)
Tested statistics and machine learning based regression models, delivered highly accurate and interpretable VM model
Developed a system for process engineers to track OES times/wavelengths affecting metrology
Supply Chain Intern, Intel
February 2023 – June 2023
Tested local instances of Large Language Models for user interaction with spreadsheets
Built Virtual Metrology model using custom time series segmentation with trace data
Created system for subfab pump failure prediction
Data Science Intern, Samsung Austin Semiconductor
June 2019 – August 2019
Implemented advanced time series segmentation methods
Tested methods on large-scale data sets (~1TB /day) with high-performance computers
Wrote code to identify similarities between wafer defect maps
Logistics Intern, Goodman Manufacturing
June 2018 – August 2018
Wrote SQL code to validate and measure distributor-reported data
Implemented processes to improve distributor data for future logistics planning
Built seasonal sales and sell-through forecasts in R
Created Tableau dashboard to display validation and forecast results for end users
Trading Intern, TransMarketGroup
May 2017 – August 2017
Built empirical models of Mortgage-Backed Securities
Participated in company course on commodities and futures markets
Wrote optimization tools for nonlinear models
Robotics Intern, Applied Materials
February 2018 – June 2018
Developed time series segmentation code to identify robot move states
Used wavelet transform to diagnose robot health based on vibration data
Set up logistic regression to predict robot failures