About
I'm fascinated by the human brain, how it builds our experience of the world, and how that machinery can falter. My background spans molecular neuroscience, mood and depression, and computational modeling. Now I'm focused on the future: building AI for health that turns insights about the brain into tools people can actually use. I care especially about making these tools inclusive, so they work across different cultures and communities. My goal is to connect neuroscience, data, and translation into research that improves brain health and mental wellbeing for everyone.
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.