Namyong Park

Postdoctoral Researcher at Meta AI
Email: park.namyong at gmail.com

About

I am a Postdoctoral Researcher at Meta AI, where I develop efficient methods for effective optimization and representation learning of graph neural networks and large language models. 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

Graph Learning, Machine Learning, Representation Learning, Knowledge Reasoning, Knowledge Graphs.

Publications

  1. Large Graph Generative Models
    Yu Wang, Ryan Rossi, Namyong Park, Huiyuan Chen, Nesreen Ahmed, Puja Trivedi, Franck Dernoncourt, Danai Koutra, and Tyler Derr
    arXiv 2024
  2. 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 (to appear)
  3. 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
  4. 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
  1. 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
  2. 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
  3. 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
  4. 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
  5. On Graph Time-Series Representations for Temporal Networks
    Ryan Rossi, Nesreen Ahmed, and Namyong Park
    The ACM Web Conference (TheWebConf) 2023
  6. 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
  7. 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
  1. 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
  2. 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
  3. 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) 2022
  4. 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
  5. 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
  1. 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
  2. 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
  3. 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
  4. 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
    IEEE International Conference on Data Engineering (ICDE) 2021
  1. 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
  2. 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
  3. PACC: Large scale connected component computation on Hadoop and Spark
    Ha-Myung Park, Namyong Park, Sung-Hyon Myaeng, and U Kang
    PLOS ONE 2020
  4. Dropout Prediction over Weeks in MOOCs by Learning Representations of Clicks and Videos
    Byungsoo Jeon*, and Namyong Park*
    AI4EDU (Artificial Intelligence for Education) Workshop at AAAI 2020
  5. Dropout Prediction over Weeks in MOOCs via Interpretable Multi-Layer Representation Learning
    Byungsoo Jeon*, Namyong Park*, and Seojin Bang*
    AI4EDU (Artificial Intelligence for Education) Workshop at AAAI 2020
  1. 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
  2. Fast and scalable method for distributed Boolean tensor factorization
    Namyong Park, Sejoon Oh, and U Kang
    The VLDB Journal 2019
  3. 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
  4. 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, Yon Su Kim, Hyung Jin Yoon, and Hajeong Lee
    Cancer Medicine 2019
  1. 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
  2. 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
  1. Fast and Scalable Distributed Boolean Tensor Factorization
    Namyong Park, Sejoon Oh, and U Kang
    IEEE International Conference on Data Engineering (ICDE) 2017
  2. 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
  1. BIGtensor: Mining Billion-Scale Tensor Made Easy
    Namyong Park*, Byungsoo Jeon*, Jungwoo Lee, and U Kang
    ACM International Conference on Information and Knowledge Management (CIKM) 2016
  2. 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
  3. KIISE
    A Distributed Vertex Rearrangement Algorithm for Compressing and Mining Big Graphs
    Namyong Park, Chiwan Park, and U Kang
    Journal of KIISE 2016
  1. 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
  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
  1. Evolving the Best Known Approximation to the Q Function
    Dao Ngoc Phong, Nguyen Xuan Hoai, Robert Ian McKay, Constantin Siriteanu, Nguyen Quang Uy, and Namyong Park
    Genetic and Evolutionary Computation Conference (GECCO) 2012

Education

Research & Work Experience

  • Adobe Research, San Jose, CA • May 2021–Aug 2021
    Research Intern
  • Microsoft Research, Redmond, WA • June 2020–Aug 2020
    Research Intern
  • Amazon, Seattle, WA • May 2019–Aug 2019
    Applied Scientist Intern
  • Amazon, Seattle, WA • May 2018–Aug 2018
    Applied Scientist Intern
  • Seoul National University, Seoul, Korea • Oct 2015–Jul 2017
    Research Staff • Data Mining Lab
  • SAP Labs Korea, Seoul, Korea • Jan 2015–Jan 2016
    Software Developer
  • Fancy, New York, NY, USA (remote) • Aug 2013–Dec 2014
    Software Engineer