About
I'm a senior majoring in Mechanical Engineering with a minor in Entrepreneurship at Vanderbilt. My background is in hardware — I've built agricultural robotics and designed sensor and navigation systems for autonomous field vehicles — but working in this lab has pulled me toward AI, and specifically toward the question of how closely machine representations can be made to match human ones. I'm interested in representation learning, alignment, and what artificial systems can borrow from how people actually learn. After graduating I'd like to work at the intersection of engineering and machine learning, building systems designed around the way humans think rather than adapted to them afterward.
Research Interests
Current Research Projects
Developmental Adaptive Learning Experiment (DALE)
DALE investigates whether the way children learn can make machine learning more efficient. Using a contrastive learning task in which participants pair spoken Danish animal words with images, we quantify how the benefit of adaptive versus random learning changes from childhood through adulthood. The order and pace at which learners master each word are then used to build epoch-specific training curricula for vision-language models: we fine-tune CLIP under adaptive, random, and mixed curricula and compare them against standard training regimes, measuring both task performance and how closely the model's internal representations come to resemble human ones. The broader question is whether human developmental learning trajectories carry structure that is genuinely useful to artificial systems, and whether AI can serve as a testable model of human cognitive development.