This research explores whether altering bodily sensations can change emotional experiences. By manipulating perceived heartbeat, blinking, and muscle tension through wearable devices and virtual reality, it demonstrates that emotions can be reshaped without changing physiology, opening new possibilities for treating trauma, anxiety, eating disorders, and other mental health conditions

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.