Jackie Baek

Jackie Baek is an Assistant Professor of Technology, Operations, and Statistics at the Leonard N. Stern School of Business, New York University. She joined Stern in January 2023. Her research focuses on developing machine learning algorithms for decision making and examining the societal implications of such algorithms. Her work addresses issues in algorithmic fairness, global health, revenue management, and transportation. Baek earned her PhD in Operations Research from MIT in 2022 and a BMath from the University of Waterloo. Prior to joining NYU Stern, she served as a research fellow at the Simons Institute for the Theory of Computing at the University of California, Berkeley.

Jackie Baek is a researcher at NYU Stern with a focus on decision-making under uncertainty, algorithmic fairness, and behavioral economics. Her work explores the intersection of operations research, machine learning, and social dynamics, with applications in healthcare, retail, and policy. She investigates how algorithms can be designed to balance fairness and efficiency, particularly in settings involving personalized interventions and resource allocation. Her research also addresses the limitations of machine learning models in real-world scenarios, such as the challenges of diffusion modeling and statistical discrimination. Baek's contributions span both theoretical and applied domains, emphasizing the importance of human-AI collaboration and the ethical implications of algorithmic decision-making.