Namyong Park

About

I am a Research Scientist at Meta AI, where I develop methods for efficient optimization and representation learning of large language models and graph neural networks. I received my PhD in Computer Science from Carnegie Mellon University, where I was advised by Prof. Christos Faloutsos. During my PhD, I also spent time at Adobe Research (2021), Microsoft Research (2020), and Amazon (2019, 2018) as a research intern. My PhD studies were supported by the Bloomberg Data Science PhD Fellowship and the ILJU Foundation PhD Fellowship.

Research Interests

Machine Learning, Graph Learning, Representation Learning, Knowledge Reasoning, Large Language Models, Efficient Deep Learning

Publications

  1. ACL
    From Selection to Generation: A Survey of LLM-based Active Learning
    Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie, ..., Namyong Park, et al.
    Association for Computational Linguistics (ACL) 2025 (to appear)
    PDF
  2. ACL
    GUI Agents: A Survey
    Dang Nguyen, Jian Chen, Yu Wang, Gang Wu, Namyong Park, et al.
    Findings of the Association for Computational Linguistics (ACL) 2025 (to appear)
    PDF
  3. TNSE
    Edges Matter: An Analysis of Graph Time-Series Representations for Temporal Networks
    Hongjie Chen, Ryan Rossi, Nesreen Ahmed, Namyong Park, Yu Wang, Tyler Derr, and Hoda Eldardiry
    IEEE Transactions on Network Science and Engineering (TNSE) 2025 (to appear)
  4. ICLR
    A Large-scale Training Paradigm for Graph Generative Models
    Yu Wang, Ryan Rossi, Namyong Park, Huiyuan Chen, Nesreen Ahmed, Puja Trivedi, Franck Dernoncourt, Danai Koutra, and Tyler Derr
    International Conference on Learning Representations (ICLR) 2025
    PDF Code
  5. NAACL
    Demystifying the Power of Large Language Models in Graph Generation
    Yu Wang, Ryan Rossi, Namyong Park, Nesreen Ahmed, Danai Koutra, Franck Dernoncourt, and Tyler Derr
    Findings of the Association for Computational Linguistics: NAACL 2025
    PDF Code
  1. EMNLP
    Memory-Efficient Fine-Tuning of Transformers via Token Selection
    Antoine Simoulin*, Namyong Park*, Xiaoyi Liu, and Grey Yang (* denotes equal contribution)
    Conference on Empirical Methods in Natural Language Processing (EMNLP) 2024
    PDF Code
  2. ICML
    Editing Partially Observable Networks via Graph Diffusion Models
    Puja Trivedi, Ryan Rossi, David Arbour, Tong Yu, Franck Dernoncourt, Sungchul Kim, Nedim Lipka, Namyong Park, Nesreen Ahmed, and Danai Koutra
    International Conference on Machine Learning (ICML) 2024
    PDF Code
  3. ICLR
    Forward Learning of Graph Neural Networks
    Namyong Park, Xing Wang, Antoine Simoulin, Shuai Yang, Grey Yang, Ryan Rossi, Puja Trivedi, and Nesreen Ahmed
    International Conference on Learning Representations (ICLR) 2024
    PDF Code
  4. TKDD
    Fairness-Aware Graph Neural Networks: A Survey
    April Chen, Ryan Rossi, Namyong Park, Puja Trivedi, Yu Wang, Tong Yu, Sungchul Kim, Franck Dernoncourt, and Nesreen Ahmed
    ACM Transactions on Knowledge Discovery from Data (TKDD) 2024
    PDF
  1. NeurIPS
    GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
    Namyong Park, Ryan Rossi, Xing Wang, Antoine Simoulin, Nesreen Ahmed, and Christos Faloutsos
    Neural Information Processing Systems (NeurIPS) 2023
    PDF Code Data Poster Slides
  2. CIKM
    CallMine: Fraud Detection and Visualization of Million-Scale Call Graphs
    Mirela T. Cazzolato, Saranya Vijayakumar, Meng-Chieh Lee, Catalina Vajiac, Namyong Park, Pedro Fidalgo, Agma J. M. Traina, and Christos Faloutsos
    ACM International Conference on Information and Knowledge Management (CIKM) 2023
    PDF Code
  3. ES-FoMo@ICML
    Memory-Efficient Selective Fine-Tuning
    Antoine Simoulin, Namyong Park, Xiaoyi Liu, and Grey Yang
    ES-FoMo (Efficient Systems for Foundation Models) Workshop at ICML 2023
    PDF Talk
  4. ICLR
    MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-Learning
    Namyong Park, Ryan Rossi, Nesreen Ahmed, and Christos Faloutsos
    International Conference on Learning Representations (ICLR) 2023
    PDF Code Data Poster Slides
  5. TheWebConf
    On Graph Time-Series Representations for Temporal Networks
    Ryan Rossi, Nesreen Ahmed, and Namyong Park
    The ACM Web Conference (TheWebConf) Companion 2023
    PDF
  6. AAAI
    TgrApp: Anomaly Detection and Visualization of Large-Scale Call Graphs
    Mirela T. Cazzolato, Saranya Vijayakumar, Xinyi Zheng, Namyong Park, Meng-Chieh Lee, Duen Horng Chau, Pedro Fidalgo, Bruno Lages, Agma J. M. Traina, and Christos Faloutsos
    AAAI Conference on Artificial Intelligence (AAAI) 2023
    PDF Code Data
  7. TKDD
    DeltaShield: Information Theory for Human-Trafficking Detection
    Catalina Vajiac, Meng-Chieh Lee, Aayushi Kulshrestha, Sacha Levy, Namyong Park, Andreas M. Olligschlaeger, Cara Jones, Reihaneh Rabbany, and Christos Faloutsos
    ACM Transactions on Knowledge Discovery from Data (TKDD) 2023
    PDF Code
  1. IEEE BigData
    TgraphSpot: Fast and Effective Anomaly Detection for Time-Evolving Graphs
    Mirela T. Cazzolato, Saranya Vijayakumar, Xinyi Zheng, Namyong Park, Meng-Chieh Lee, Pedro Fidalgo, Bruno Lages, Agma J. M. Traina, and Christos Faloutsos
    IEEE International Conference on Big Data (IEEE BigData) 2022
    PDF Code
  2. TheWebConf
    CGC: Contrastive Graph Clustering for Community Detection and Tracking
    Namyong Park, Ryan Rossi, Eunyee Koh, Iftikhar Ahamath Burhanuddin, Sungchul Kim, Fan Du, Nesreen Ahmed, and Christos Faloutsos
    The ACM Web Conference (TheWebConf) 2022
    PDF Data
  3. TheWebConf
    VisPaD: Visualization and Pattern Discovery for Fighting Human Trafficking
    Pratheeksha Nair, Yifei Li, Catalina Vajiac, Andreas M. Olligschlaeger, Meng-Chieh Lee, Namyong Park, Duen Horng Chau, Christos Faloutsos, and Reihaneh Rabbany
    The ACM Web Conference (TheWebConf) Companion 2022
    PDF Code Data
  4.  VIS 
    TrafficVis: Visualizing Organized Activity and Spatio-Temporal Patterns for Detecting and Labeling Human Trafficking
    Catalina Vajiac, Duen Horng Chau, Andreas M. Olligschlaeger, Rebecca Mackenzie, Pratheeksha Nair, Meng-Chieh Lee, Yifei Li, Namyong Park, Reihaneh Rabbany, and Christos Faloutsos
    IEEE Visualization Conference (VIS) 2022
    PDF Code
  5. WSDM
    EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs
    Namyong Park, Fuchen Liu, Purvanshi Mehta, Dana Cristofor, Christos Faloutsos, and Yuxiao Dong
    ACM International Conference on Web Search and Data Mining (WSDM) 2022
    PDF Code Data
  1. arXiv
    Knowledge-Guided Dynamic Systems Modeling: A Case Study on Modeling River Water Quality
    Namyong Park, Minhyeok Kim, Nguyen Xuan Hoai, Robert I. McKay, and Dong-Kyun Kim
    arXiv 2021
    PDF
  2.  VIS 
    TrafficVis: Fighting Human Trafficking through Visualization
    Catalina Vajiac, Andreas M. Olligschlaeger, Yifei Li, Pratheeksha Nair, Meng-Chieh Lee, Namyong Park, Reihaneh Rabbany, Duen Horng Chau, and Christos Faloutsos
    IEEE Visualization Conference (VIS) 2021
    PDF
  3. ICDE
    Knowledge-Based Dynamic Systems Modeling: A Case Study on Modeling River Water Quality
    Namyong Park, Minhyeok Kim, Nguyen Xuan Hoai, Robert I. McKay, and Dong-Kyun Kim
    IEEE International Conference on Data Engineering (ICDE) 2021
    PDF
  4. ICDE
    InfoShield: Generalizable Information-Theoretic Human-Trafficking Detection
    Meng-Chieh Lee*, Catalina Vajiac*, Aayushi Kulshrestha, Sacha Levy, Namyong Park, Cara Jones, Reihaneh Rabbany, and Christos Faloutsos (* denotes equal contribution)
    IEEE International Conference on Data Engineering (ICDE) 2021
    PDF Code
  1. ICDM
    J-Recs: Principled and Scalable Recommendation Justification
    Namyong Park, Andrey Kan, Christos Faloutsos, and Xin Luna Dong
    IEEE International Conference on Data Mining (ICDM) 2020
    PDF
  2.  KDD 
    MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals
    Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, and Christos Faloutsos
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2020
    PDF
  3. PLOS ONE
    PACC: Large scale connected component computation on Hadoop and Spark
    Ha-Myung Park, Namyong Park, Sung-Hyon Myaeng, and U Kang
    PLOS ONE 2020
    PDF Code Data
  4. AI4EDU@AAAI
    Dropout Prediction over Weeks in MOOCs by Learning Representations of Clicks and Videos
    Byungsoo Jeon*, and Namyong Park* (* denotes equal contribution)
    AI4EDU (Artificial Intelligence for Education) Workshop at AAAI 2020
    PDF
  5. AI4EDU@AAAI
    Dropout Prediction over Weeks in MOOCs via Interpretable Multi-Layer Representation Learning
    Byungsoo Jeon*, Namyong Park*, and Seojin Bang* (* denotes equal contribution)
    AI4EDU (Artificial Intelligence for Education) Workshop at AAAI 2020
    PDF
  1.  KDD 
    Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
    Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, and Christos Faloutsos
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2019
    PDF Poster Slides
  2. VLDBJ
    Fast and scalable method for distributed Boolean tensor factorization
    Namyong Park, Sejoon Oh, and U Kang
    The VLDB Journal 2019
    PDF Code Data
  3. TPDS
    High-Performance Tucker Factorization on Heterogeneous Platforms
    Sejoon Oh, Namyong Park, Jun-Gi Jang, Lee Sael, and U Kang
    IEEE Transactions on Parallel and Distributed Systems (TPDS) 2019
    PDF Code Data
  4.   CM  
    Acute kidney injury predicts all-cause mortality in patients with cancer
    Eunjeong Kang, Minsu Park, Peong Gang Park, Namyong Park, Younglee Jung, U Kang, Hee Gyung Kang, Dong Ki Kim, Kook-Hwan Oh, Kwon Wook Joo, and 3 more authors
    Cancer Medicine 2019
    PDF
  1. PLOS ONE
    Predicting acute kidney injury in cancer patients using heterogeneous and irregular data
    Namyong Park, Eunjeong Kang, Minsu Park, Hajeong Lee, Hee-Gyung Kang, Hyung-Jin Yoon, and U. Kang
    PLOS ONE 2018
    PDF
  2. ICDE
    Scalable Tucker Factorization for Sparse Tensors - Algorithms and Discoveries
    Sejoon Oh, Namyong Park, Lee Sael, and U Kang
    IEEE International Conference on Data Engineering (ICDE) 2018
    PDF Code Data
  1. ICDE
    Fast and Scalable Distributed Boolean Tensor Factorization
    Namyong Park, Sejoon Oh, and U Kang
    IEEE International Conference on Data Engineering (ICDE) 2017
    PDF Code Data
  2. SIGMOD
    BePI: Fast and Memory-Efficient Method for Billion-Scale Random Walk with Restart
    Jinhong Jung, Namyong Park, Lee Sael, and U Kang
    ACM International Conference on Management of Data (SIGMOD) 2017
    PDF Code Data
  1. CIKM
    BIGtensor: Mining Billion-Scale Tensor Made Easy
    Namyong Park*, Byungsoo Jeon*, Jungwoo Lee, and U Kang (* denotes equal contribution)
    ACM International Conference on Information and Knowledge Management (CIKM) 2016
    PDF Code Data
  2. ICDM
    Partition Aware Connected Component Computation in Distributed Systems
    Ha-Myung Park, Namyong Park, Sung-Hyon Myaeng, and U Kang
    IEEE International Conference on Data Mining (ICDM) 2016
    PDF Code Data
  3. KIISE
    A Distributed Vertex Rearrangement Algorithm for Compressing and Mining Big Graphs
    Namyong Park, Chiwan Park, and U Kang
    Journal of KIISE 2016
    PDF Data
  1. ECOMOD
    Improvement of complex and refractory ecological models: Riverine water quality modelling using evolutionary computation
    MinHyeok Kim, Namyong Park, RI Bob McKay, Haisoo Shin, Yun-Geun Lee, Kwang-Seuk Jeong, and Dong-Kyun Kim
    Ecological Modelling 2014
    PDF
  1. CEC
    Cutting Evaluation Costs: An Investigation into Early Termination in Genetic Programming
    Namyong Park, Kangil Kim, and Robert I. McKay
    IEEE Congress on Evolutionary Computation (CEC) 2013
    PDF

    Education

    Previous Research & Work Experience