The CMU PhD in AI unifies machine learning, reasoning, language, and human factors. Students design neuro-symbolic architectures, formalize value-alignment, and evaluate societal impacts through policy fellowships. Cross-disciplinary mentorship from philosophy, cognitive science, and public policy ensures your algorithms are both powerful and principled.
Neuro-symbolic planner for household robots in cluttered scenes
Formal logic framework verifying safety in hierarchical policies
LLM-based argument-mapping tool for policy deliberation
Bayesian inverse-game-theory model inferring human preferences
Emotion-aware dialogue agent evaluated on mental-health support corpora
Multi-agent decision-making benchmark in mixed-motive scenarios
Graphical-model fusion of vision and language for embodied QA
Causal-counterfactual reasoning module reducing bias in recommendations
Curriculum for teaching AI literacy in K-12 classrooms via block coding
White paper on global AI compute governance frameworks
Adaptive meta-controller switching between symbolic and neural components
Measurement study of energy and water footprint of frontier models
Zero-shot policy generalization across simulated ethical dilemmas
Interactive theorem prover guiding LLM reasoning chains
Citizen-assembly app using AI facilitation for consensus building
Study core AI and shape its human-centric future at CMU.
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