معرفی
Jackie Baek is an Assistant Professor in the Department of Technology, Operations, and Statistics at the Leonard N. Stern School of Business, New York University, where she joined in January 2023. Her research bridges machine learning, operations research, and societal impact, focusing on algorithmic fairness, decision-making systems, and data-driven applications in healthcare and online platforms.
- Ph.D., Operations Research, Massachusetts Institute of Technology (2022)
- Bachelor, Mathematics, University of Waterloo
Her research interests span Machine Learning, Algorithmic Fairness, Data-Driven Operations, Healthcare Analytics, Revenue Management, and Transportation Systems. She develops algorithms that improve decision-making while examining the societal consequences of algorithmic systems, particularly in hiring and health interventions. Her work combines theoretical rigor with practical applications, often involving collaborations with institutions like the Simons Institute at UC Berkeley.
The recent publications reflect a strong trend in fairness-aware machine learning, bandit algorithms, and behavioral health interventions. Her work on TS-UCB improves Thompson sampling efficiency, while papers on statistical discrimination and algorithmic monoculture highlight ethical concerns in hiring algorithms. Other works apply machine learning to global health, such as COVID-19 forecasting and personalized behavioral interventions. These contributions appear in top-tier journals and conferences including Management Science, Operations Research, PNAS, and ACM FAccT.
Her scientific awards include:
- Winner, Pierskalla Best Paper Award (2024)
- Second place, MSOM Student Paper Competition (2022)
- Finalist, George Nicholson Student Paper Competition (2021)
- Finalist, RMP Jeff McGill Student Paper Award (2021)
- Honorable Mention, MIT ORC Best Student Paper Award (2021)
- Oral presentation at EAAMO (2021)
Jackie Baek has advised or collaborated on research with students and scholars such as Hamsa Bastani, Shihan Chen, and Vivek Farias. She has contributed to forecasting efforts through the COVID-19 Forecast Hub and presented her work at venues like BIRS. While no specific grants are mentioned, her publications suggest support from institutions like MIT, Simons Institute, and NYU Stern. She is actively involved in research groups focusing on dynamic allocation and matching, as evidenced by her BIRS talk in 2025.
She maintains an active research presence with code available on GitHub and participates in interdisciplinary collaborations across computer science, operations, and social impact. Her lab or research team appears to focus on algorithmic fairness and decision systems, likely involving graduate students and postdocs in ongoing projects related to fairness, learning, and operations.
