Changhun Lee

Postdoctoral Scientist, Columbia University & UNIST

cl4670@cumc.columbia.edu, changhun@unist.ac.kr

Bio

I am a postdoctoral scientist at Columbia University. I received my Ph.D degree in February 2023 and won the best researcher award for my thesis titled "Tackling Three Problems in Controlled Sequence Generations: Bridging Reinforcement Learning with Language Models.".

My research interest covers LLMs, RL, and their application in AI4Healthcare & AI4Science. In particular, leveraging RL to align LLMs with human preferences is of the most interest that I am currently focusing on. In this regard, my research topics range from fundamental to applied LLMs+RL studies, including but not limited to:

  • Developing fundamental frameworks, algorithms, and building blocks for LLMs.
  • Designing alignment mechanisms to enhance the interaction between LLMs and humans.
  • Deploying an LLM-based service to solve real-world problems in healthcare and science domains.

    • Publications

    Most recent publications on Google Scholar.
    ‡ indicates equal contribution.

    Towards Pareto-Efficient RLHF: Paying Attention to a Few High-Reward Samples with Reward Dropout

    Changhun Lee, Chiehyeon Lim

    Findings of the Association for Computational Linguistics: EMNLP 2024

    Paper Code

    Repurformer: Transformers for Repurposing-Aware Molecule Generation

    Changhun Lee, Gyumin Lee

    Language + Molecules @ ACL 2024 (Oral)

    Paper

    Recommendation in Offline Stores: A Gamification Approach for Learning the Spatiotemporal Representation of Indoor Shopping

    Jongkyung Shin‡, Changhun Lee‡, Chiehyeon Lim, Yunmo Shin, Junseok Lim

    KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. (Oral)

    Paper Code Data

    MIND dataset for diet planning and dietary healthcare with machine learning: Dataset creation using combinatorial optimization and controllable generation with domain experts

    Changhun Lee‡, Soohyeok Kim‡, Sehwa Jeong, Jayun Kim, Yeji Kim, Chiehyeon Lim, Minyoung Jung

    Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS Datasets and Benchmarks 2021)

    Paper Code Data

    Diet Planning with Machine Learning: Teacher-forced REINFORCE for Composition Compliance with Nutrition Enhancement

    Changhun Lee, Soohyeok Kim, Chiehyeon Lim, Jayun Kim, Yeji Kim, Minyoung Jung

    KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining.

    Paper Code

    From technological development to social advance: A review of Industry 4.0 through machine learning

    Changhun Lee, Chiehyeon Lim

    Technological Forecasting and Social Change, 167, 120653.

    Paper

    Repurformer: Transformers for Repurposing-Aware Molecule Generation

    Changhun Lee, Gyumin Lee

    Language + Molecules @ ACL 2024 (Oral)

    Paper

    Recommendation in Offline Stores: A Gamification Approach for Learning the Spatiotemporal Representation of Indoor Shopping

    Jongkyung Shin‡, Changhun Lee‡, Chiehyeon Lim, Yunmo Shin, Junseok Lim

    KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. (Oral)

    Paper Code Data

    Challenges of Diet Planning for Children using Artificial Intelligence

    Changhun Lee‡, Soohyeok Kim, Jayun Kim, Chiehyeon Lim, Minyoung Jung

    Nutrition Research and Practice. 2022; 16.

    Paper

    MIND dataset for diet planning and dietary healthcare with machine learning: Dataset creation using combinatorial optimization and controllable generation with domain experts

    Changhun Lee‡, Soohyeok Kim‡, Sehwa Jeong, Jayun Kim, Yeji Kim, Chiehyeon Lim, Minyoung Jung

    Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS Datasets and Benchmarks 2021)

    Paper Code Data

    Diet Planning with Machine Learning: Teacher-forced REINFORCE for Composition Compliance with Nutrition Enhancement

    Changhun Lee, Soohyeok Kim, Chiehyeon Lim, Jayun Kim, Yeji Kim, Minyoung Jung

    KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining.

    Paper Code

    Human dietitians vs. Artificial intelligence: Which diet design do you prefer for your children?

    Min Jung, Chiehyeon Lim, Changhun Lee, Soohyeok Kim, Jayun Kim

    Journal of Allergy and Clinical Immunology, 147(2), AB117.

    Paper

    Towards Pareto-Efficient RLHF: Paying Attention to a Few High-Reward Samples with Reward Dropout

    Changhun Lee, Chiehyeon Lim

    Findings of the Association for Computational Linguistics: EMNLP 2024

    Paper Code

    Repurformer: Transformers for Repurposing-Aware Molecule Generation

    Changhun Lee, Gyumin Lee

    Language + Molecules @ ACL 2024 (Oral)

    Paper

    Towards Pareto-Efficient RLHF: Paying Attention to a Few High-Reward Samples with Reward Dropout

    Changhun Lee, Chiehyeon Lim

    Findings of the Association for Computational Linguistics: EMNLP 2024

    Paper Code

    Repurformer: Transformers for Repurposing-Aware Molecule Generation

    Changhun Lee, Gyumin Lee

    Language + Molecules @ ACL 2024 (Oral)

    Paper

    Recommendation in Offline Stores: A Gamification Approach for Learning the Spatiotemporal Representation of Indoor Shopping

    Jongkyung Shin‡, Changhun Lee‡, Chiehyeon Lim, Yunmo Shin, Junseok Lim

    KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. (Oral)

    Paper Code Data

    Challenges of Diet Planning for Children using Artificial Intelligence

    Changhun Lee‡, Soohyeok Kim, Jayun Kim, Chiehyeon Lim, Minyoung Jung

    Nutrition Research and Practice. 2022; 16.

    Paper

    MIND dataset for diet planning and dietary healthcare with machine learning: Dataset creation using combinatorial optimization and controllable generation with domain experts

    Changhun Lee‡, Soohyeok Kim‡, Sehwa Jeong, Jayun Kim, Yeji Kim, Chiehyeon Lim, Minyoung Jung

    Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS Datasets and Benchmarks 2021)

    Paper Code Data

    Diet Planning with Machine Learning: Teacher-forced REINFORCE for Composition Compliance with Nutrition Enhancement

    Changhun Lee, Soohyeok Kim, Chiehyeon Lim, Jayun Kim, Yeji Kim, Minyoung Jung

    KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining.

    Paper Code

    From technological development to social advance: A review of Industry 4.0 through machine learning

    Changhun Lee, Chiehyeon Lim

    Technological Forecasting and Social Change, 167, 120653.

    Paper

    Human dietitians vs. Artificial intelligence: Which diet design do you prefer for your children?

    Min Jung, Chiehyeon Lim, Changhun Lee, Soohyeok Kim, Jayun Kim

    Journal of Allergy and Clinical Immunology, 147(2), AB117.

    Paper

    Co-work Opportunities

    I am currently a postdoctoral scientist at Columbia University, living in New York City and looking for opportunities to collaborate in the fields of AI/ML, NLP, and RL. I am also open to AI4Health/AI4Science reserach. If you are interested, please feel free to contact me at cl4670@cumc.columbia.edu.

    Philosophy

    I dream of becoming a researcher who is willing to stand up against prejudice and inequality in the world with logical thinking and a scientific attitude. I am looking forward to a heart-pounding journey, working alongside great colleagues to shape a better world and a brighter future. Let me share a quote from one of my favorite scholars, Alfred Marshall:

    "Cool head, but warm heart!"

    Vitæ

    You can download my Curriculum Vitae in PDF file.

    Acknowledgement

    This website uses the website design and template by Martin Saveski

    Changhun Lee
    cl4670@cumc.columbia.edu, changhun@unist.ac.kr