Yaqing Wang’s research focuses on generalizing from a few examples, aiming to build data-efficient, adaptive, and explainable AI. Her early work established a unifying framework for few-shot learning, which highlighted the challenges of unreliable learning under sparse data and articulated three canonical scenarios—scientific scarcity, cold-start personalization, and annotation efficiency. Building on this foundation, she has developed algorithms addressing key real-world challenges: molecular property prediction and drug–drug interaction under limited data in drug discovery, recommendation models that overcome cold-start issues and are deployed in large-scale platforms, and efficient methods for intent recognition and gesture sensing where annotation or interaction is costly. Her recent work explores the synergy between meta-learning and in-context learning, and introduces personalized agents that adapt to user preferences with only a handful of interactions. These contributions reflect her continued efforts toward advancing few-shot learning in both theory and practice, with growing impact in AI for science and personalization.