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About

Aditi Raghunathan is a leading researcher at the intersection of artificial intelligence, robotics, and machine learning, whose work has profoundly shaped the discourse on large-scale AI systems. She is best known for her pivotal role as a co-author of the landmark paper "On the Opportunities and Risks of Foundation Models" (2021), which has amassed over 2,100 citations. This seminal work introduced the term "foundation models" to describe powerful, broadly-trained models like BERT and GPT-3, while critically examining their capabilities and inherent risks—a contribution that has defined a major research agenda in AI safety and ethics. Beyond her influential analysis of foundation models, Raghunathan advances practical AI through meta-reinforcement learning and robotics. Her research on "Explore then Execute" (2020) develops algorithms that enable agents to adapt to new tasks without explicit rewards by leveraging shared structure across environments. She further applies these principles to assistive robotics (2022), focusing on learning representations that allow robots to generalize their skills when helping humans in diverse, real-world settings. Through her work, Raghunathan bridges the gap between foundational AI theory and embodied, generalizable robotic assistance.