Georgia Tech’s ML PhD—joint between CS, ECE, ISyE, and BME—produces researchers whose papers dominate NeurIPS and Science. Doctoral scholars design theoretically grounded architectures, tackle AI safety, and deploy edge models on drones tracking poachers.
Provably robust transformer under adversarial patch attacks
Energy-adaptive federated learning on low-power IoT
Causal representation learning for real-world vision tasks
Sparse mixture-of-experts LLM scaling laws study
Differentially private graph neural nets for health data sharing
Meta-RL pipeline for autonomous warehouse robotics
Self-supervised speech model in endangered languages
Neuromorphic spiking network for sub-mW keyword spotting
Alignment taxonomy and evaluation suite for embodied AI agents
Bayesian deep-learning uncertainty metrics in medical imaging
Hardware/software co-design for analog in-memory training
Generative-adversarial weather downscaling benchmark
LLM red-teaming dataset generator via reinforcement-learning
Network pruning theory for lottery-ticket at trillion-param scale
Curriculum learning scheduler for multi-task brain-computer decoding
Invent learning algorithms that power safe, efficient AI everywhere.
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