Home to the world’s first ML department, CMU’s PhD in Machine Learning trains scholars to ground practical advances in rigorous mathematics. From optimal transport theory to multi-modal foundation models, you’ll collaborate across robotics, comp-bio, and policy labs, often spinning off open-source libraries adopted globally. Students chair NeurIPS workshops, consult with UN AI ethics panels, and graduate into faculty posts or research-lead positions at frontier labs.
Convergence proof for federated SGD under non-IID user distributions
Neural architecture search minimizing FLOPs via differentiable proxies
Causal representation learning for robust climate-impact forecasting
Certified defenses against multimodal adversarial attacks
Sparse mixture-of-experts scaling laws on commodity clusters
Curriculum RL optimizing multi-robot coordination under partial observability
Bayesian meta-learning framework for rapid drug-response modeling
Information-theoretic bounds on self-supervised contrastive loss
Alignment metric quantifying policy-shock risk in large language models
Graph neural-ODEs simulating protein folding pathways
Ethical audit toolkit measuring demographic bias in vision-language systems
Green-AI scheduler allocating GPU jobs by marginal carbon intensity
Zero-shot active learning selecting maximally informative samples
Semi-parametric survival model integrating EHR and wearable streams
Policy memo on compute governance for frontier-model safety
Define tomorrow’s learning paradigms with CMU MLD.
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