Jackie Baek

I am an assistant professor at NYU Stern in the Department of Technology, Operations and Statistics. Prior to this, I was a research fellow in the Simons Institute at UC Berkeley, and I obtained my PhD in Operations Research from MIT, advised by Prof. Vivek Farias.

Broadly, my research develops machine learning algorithms for decision making, focusing on efforts to bridge theory to practice. I am interested in studying the societal impact of algorithms (e.g. fairness in algorithmic hiring), as well as designing practical algorithms for impactful applications (e.g. global health).

Email: baek (at) stern (dot) nyu (dot) edu
Office: Kaufman Management Center, 8-77
CV

Research

Working Papers

• The Feedback Loop of Statistical Discrimination
  with Ali Makhdoumi
• When Collaborative Filtering is not Collaborative: Unfairness of PCA for Recommendations
  with David Liu, Tina Eliassi-Rad
• Leveraging Reusability: Improved Competitive Ratio of Greedy for Reusable Resources
  with Shixin Wang
• Policy Optimization for Personalized Interventions in Behavioral Health
  with Justin Boutilier, Vivek Farias, Jónas Oddur Jónasson, Erez Yoeli
  Major Revision, Manufacturing and Service Operations Management

Publications

• The Limits to Learning a Diffusion Model
  with Vivek Farias, Andreea Georgescu, Retsef Levi, Tianyi Peng, Deeksha Sinha, Joshua Wilde, Andrew Zheng
  Management Science (Forthcoming)
  Preliminary version: EC 2021
• Fair Exploration via Axiomatic Bargaining (link to talk)
  with Vivek Farias
  Management Science, 2024
  Preliminary version: NeurIPS 2021 (Spotlight)
  • 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,1st ACM Conference on Equity & Access in Algorithms, Mechanisms, & Optimization, 2021
• Bifurcating Constraints to Improve Approximation Ratios for Network Revenue Management with Reusable Resources
  with Will Ma
  Operations Research, 2022
  Preliminary version: SAGT 2019
• TS-UCB: Improving on Thompson Sampling With Little to No Additional Computation
  with Vivek Farias
  AISTATS 2023
• Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
  with COVID-19 Forecast Hub
  PNAS, 2022
  • This paper resulted from contributing COVID-19 forecasts (from the paper “The Limits to Learning a Diffusion Model”) to the COVID-19 Forecast Hub.
• A Game-Theoretic Analysis of Reallocation Mechanisms for Airport Landing Slots
  with Hamsa Balakrishnan
  IEEE Transactions on Intelligent Transportation Systems, 2020