Corinna Cortes worked as a researcher at the NEC Research Institute in Princeton during the 1990s, where she conducted influential work in statistical learning theory and machine learning. She is best known for co developing support vector machines with Vladimir Vapnik, a breakthrough method that translated theoretical insights about learning from data into practical algorithms for classification and prediction. Support vector machines quickly became one of the most widely used machine learning techniques of their time, enabling reliable pattern recognition across a range of domains. Cortes’s research helped demonstrate how strong mathematical foundations could lead to robust and scalable learning methods capable of performing well on complex real world datasets. Her contributions played an important role in bridging theory and application within machine learning, and support vector machines remain a foundational technique studied in modern AI. The work carried out at NEC helped shape the early development of practical machine learning systems used across research and industry.