Sherry Yang

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About

Sherry Yang is an AI researcher working at the forefront of foundation models and decision-making systems, with a focus on bridging large-scale pretraining with real-world autonomous agents. Her work explores how models trained on vast, diverse datasets can be extended beyond traditional vision and language tasks to tackle complex sequential decision-making challenges — a frontier with profound implications for robotics, autonomous systems, and artificial general intelligence. Her most influential contribution, "Foundation Models for Decision Making: Problems, Methods, and Opportunities" (2023), has rapidly accumulated 51 citations, establishing itself as a key reference for researchers navigating how foundation models interact with agents and dynamic environments. More recently, Yang has turned her attention to video as an underexplored modality for learning world knowledge, arguing in her 2024 work that video data — like text — holds enormous untapped potential for self-supervised learning and real-world decision-making applications. Yang's research is particularly valuable for those studying the next generation of intelligent systems, as she consistently identifies gaps between current capabilities and real-world deployment, offering both rigorous problem framing and actionable research directions for the broader AI community.