This PhD defense presents research at the intersection of machine learning, reinforcement learning, social learning, affective computing, and human-AI interaction. The thesis is that social learning is a powerful mechanism for intelligence and explores how AI agents can learn from one another and from humans. Projects include intrinsic social influence rewards for multi-agent coordination, communication protocols emerging through influence, conversational agents trained from implicit human feedback such as sentiment, generative models improved through facial-expression feedback, and personalized well-being prediction from behavioral and physiological data. The thesis concludes that socially informed learning can improve coordination, adaptability, and human alignment.

This research uncovers how AI systems like GPT succeed at automatically grouping words—a task that traditionally required manual labeling. Using geometric tools such as convex hulls and Delaunay triangulation, the researcher developed an algorithm that replicates this capability, enabling powerful language models to be built with far fewer computational resources.