Hi! I am a final-year Ph.D. student in the Department of Industrial Engineering and Operations Research at Columbia University, and I am fortunate to be advised by Prof. Adam Elmachtoub.

I received my MS degree in Industrial and Systems Engineering from Korea Advanced Institute of Science and Technology (KAIST) where I was advised by Prof. Il-Chul Moon. Prior to that, I received my BS degree in Industrial and Systems Engineering and Electrical Engineering from KAIST.

Research

(*) Equal contribution. (α-β) Alphabetical order.

Conference & Journal Papers

1. Fair Aggregation in Virtual Power Plants [code]
   Fairness Energy Pricing Optimization
   Liudong Chen*, Hyemi Kim*, Adam N. Elmachtoub, and Bolun Xu
   The 9th ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2026
   

2. Learning Fair Demand Models [code]
   Fairness Pricing Machine Learning Optimization
   Adam N. Elmachtoub, Hyemi Kim, and Jonathan Y. Tan (α-β)
   The 9th ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2026
   

3. Fair Fares for Vehicle Sharing Systems [code]
   Fairness Transportation Pricing Optimization
   Adam N. Elmachtoub and Hyemi Kim (α-β)
   Operations Research, 2026
   The 8th ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2025
   🏆 Finalist, INFORMS DEI Best Student Paper Award, 2024
   🏆 Finalist, INFORMS Transportation Science and Logistics Society Best Student Paper Award, 2024
   

4. Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder [code]
   Fairness Machine Learning
   Hyemi Kim, Seungjae Shin, JoonHo Jang, Kyungwoo Song, Weonyoung Joo, Wanmo Kang, Il-Chul Moon
   Association for the Advancement of Artificial Intelligence 2021
   

5. Black-Box EM Algorithm for Estimating Latent States of High-Speed Vehicles
   Machine Learning
   Yoon-Yeong Kim, Hyemi Kim, WonSung Lee, Han-Lim Choi, Il-Chul Moon
   American Institute of Aeronautics and Astronautics
   

6. Neutralizing Gender Bias in Word Embedding with Latent Disentanglement and Counterfactual Generation
   Fairness Machine Learning
   Seungjae Shin, Kyungwoo Song, Joonho Jang, Hyemi Kim, Weonyoung Joo, Il-Chul Moon
   Findings of Empirical Methods in Natural Language Processing (Findings of EMNLP) 2020
   

7. Deep Generative Positive-Unlabeled Learning under Selection Bias
   Machine Learning
   ByeongHu Na, Hyemi Kim, Kyungwoo Song, Weonyoung Joo, Yoonyeong Kim, Il-Chul Moon
   Conference on Information and Knowledge Management (CIKM) 2020
   

Workshop Paper

1. Improving Group-based Robustness and Calibration via Ordered Risk and Confidence Regularization
   Machine Learning
   Seungjae Shin, Byeonghu Na, HeeSun Bae, JoonHo Jang, Hyemi Kim, Kyungwoo Song, Youngjae Cho, Il-chul Moon
   ICML 2022: Workshop on Spurious Correlations, Invariance and Stability
   

Teaching

• Introduction to Probability and Statistics, Columbia IEOR (Fall 2024)
• Stochastic Models, Columbia IEOR (Spring 2024)
• Analytics in Action, Columbia Business School (Fall 2023)
• Transportation Analytics & Logistics, Columbia IEOR (Spring 2023)
• Data Structure, KAIST ISysE (Fall 2018, Fall 2019)

Fellowships & Awards

I am incredibly grateful for the generous support of the following fellowships and the recognition of these awards.

• Columbia-Dream Sports AI Innovation Fellowship, Columbia University (2025)
• Finalist, INFORMS DEI Best Student Paper Award (2024)
• Finalist, INFORMS Transportation Science and Logistics Best Student Paper Award (2024)
• Finalist, INFORMS 2023 BSS Data Challenge Competition (2023) – Decoupling Price and Demand Estimation for Profit Optimization
• Deming Doctoral Fellowship, Columbia Business School (2023)
• Songhyun Award, KAIST (2019)
• Presidential Science Scholarship, KFAS (2012)