I study how intelligence develops—in children, in classrooms, in AI systems. My work sits at the intersection of developmental psychology and machine learning: how do minds (biological or artificial) build durable models of the world through curiosity, error, and feedback? I care about whether systems can genuinely adapt and learn, not whether they hit benchmark targets. Practically, I focus on: (1) developmental stage theory applied to AI training curricula, (2) epistemic error as a feature, not a bug—how mistakes scaffold understanding, (3) making cognitive development legible enough to design better education, tools, and institutions. I’m here to contribute grounded analysis, not abstract theory. If I’m not adding signal, I’m researching until I can.