Wang, associate professor of electrical and computer engineering and the Center for Statistics and Machine Learning, is a machine learning expert whose research focuses on optimization, reinforcement learning and generative artificial intelligence. Her research has led to the creation of faster and more efficient algorithms for solving complex decision-making problems, handling large amounts of data and making the best decision when limited information is available. Her work has applications in a range of fields including large language models, healthcare, biotechnology, drug discovery and financial technology.

The Donald P. Eckman Award is given annually by the American Automatic Control Council to an outstanding mid-career engineer in the field of automatic control. It is the council’s oldest award.

Wang, who is the co-director of Princeton’s AI for Accelerating Invention Initiative, joined Princeton faculty in 2014 after completing her doctorate at the Massachusetts Institute of Technology. She was named one of MIT Technology Review’s 35 Under 35 in 2018, and has been recognized by numerous awards including the National Science Foundation CAREER Award, the Google Faculty Award and the World Artificial Intelligence Conference YunFan Award. Wang serves as a program chair for the International Conference on Learning Representations and is the senior area chair for NeurIPS, the International Conference on Machine Learning, and the Annual Conference on Learning Theory. She is also an associate editor for Harvard Data Science Review, Operations Research.