Machine Learning. Generative AI. Data Science.
Understanding complex systems and measuring what matters is the common thread that runs through my professional and academic experience. I first learned to trust evidence over assumption and to test ideas through experimentation as a quantitative scientist. Years in performance evaluation and root cause analysis then taught me to ask why systems behave as they do, identify what drives outcomes and determine what can be improved. Graduate work in Data Science gave me the technical depth to apply that thinking to Machine Learning and Artificial Intelligence, at greater scale and in new ways.
The projects below put that thinking to work, applying Machine Learning, Generative AI and Statistical Analysis to problems ranging from prediction and experimentation to information retrieval and intelligent systems. Across them, my focus remains consistent. Building the model is only part of the work. The rest is defining what success means, weighing the tradeoffs and using evidence to improve the outcome.
A real-time gaze-to-speech tool that gives people with physical disabilities a voice using only their eye movements.
Helping engineering and marketing teams get fast, audience-tailored answers from GenAI research documents.
Using XGBoost to predict wildfire occurrence across 3, 7 and 14-day horizons.
A bidding agent for a simulated second-price ad auction that infers each user's value from observed behavior.
A research proposal examining racial disparities in clinician empathy during childbirth.
A multiple regression analysis investigating whether age predicts sleep duration, using CDC survey data.
A program that finds every valid word in a Scrabble rack, including wildcards, then scores and ranks them.
A convolutional neural network that reads retinal images and flags which patients should be referred for treatment.
A Neo4j graph of global refugee flows that uses network centrality to surface the top sending and receiving countries.
Interactive D3.js visualization investigating how caffeine intake relates to stress across 20 countries.