Research Interests
Scalable Graph Mining, Deep Learning for Graphs, Knowledge Reasoning, Knowledge Graph, Machine Learning

Publications
  • InfoShield: Generalizable Information-Theoretic Human-Trafficking Detection
    Meng-Chieh Lee*, Catalina Vajiac*, Aayushi Kulshrestha, Sacha Levy, Namyong Park, Cara Jones, Christos Faloutsos, and Reihaneh Rabbany
    IEEE International Conference on Data Engineering (ICDE) 2021 (to appear).
    [paper]
  • 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 (to appear).
    [paper]
  • 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.
    [paper]
  • PACC: Large Scale Connected Component Computation on Hadoop and Spark
    Ha-Myung Park, Namyong Park, Sung-Hyon Myaeng, and U Kang
    PLOS ONE 2020.
    [paper | code & datasets]
  • Dropout Prediction over Weeks in MOOCs by Learning Representations of Clicks and Videos
    Byungsoo Jeon*, and Namyong Park*
    AAAI-20 Workshop on Artificial Intelligence for Education 2020.
    [paper]
  • Dropout Prediction over Weeks in MOOCs via Interpretable Multi-Layer Representation Learning
    Byungsoo Jeon*, Namyong Park*, and Seojin Bang
    AAAI-20 Workshop on Artificial Intelligence for Education 2020.
    [paper]
  • 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.
    [paper]
  • Fast and Scalable Method for Distributed Boolean Tensor Factorization
    Namyong Park, Sejoon Oh, and U Kang
    The VLDB Journal 2019.
    [paper | code & datasets]
  • 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.
    [paper | code & datasets]
  • Acute kidney injury predicts all-cause mortality in patients with cancer
    Eunjeong Kang, Minsu Park, Peonggang Park, Namyong Park, Younglee Jung, U Kang, Hee Kyung Kang, Dong Ki Kim, Kwon Wook Joo, Yon Su Kim, Hyung Jin Yoon and Hajeong Lee
    Cancer Medicine 2019.
    [paper]
  • 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.
    [paper]
  • 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.
    [paper | code & datasets]
  • Fast and Scalable Distributed Boolean Tensor Factorization
    Namyong Park, Sejoon Oh, and U Kang
    IEEE International Conference on Data Engineering (ICDE) 2017.
    [paper | code & datasets]
  • 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.
    [paper | code & datasets]
  • 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.
    [paper | code & datasets]
  • 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.
    [paper | code & datasets]
  • A Distributed Vertex Rearrangement Algorithm for Compressing and Mining Big Graphs
    Namyong Park, Chiwan Park, and U Kang
    Journal of KIISE 2016.
    [paper | datasets]
  • Improvement of complex and refractory ecological models: riverine water quality modelling using evolutionary computation
    MinHyeok Kim, Namyong Park, R.I. (Bob) McKay, Haisoo Shin, Yun-Geun Lee, Kwang-Seuk Jeong, and Dong-Kyun Kim
    Ecological Modelling 2014.
    [paper]
  • Cutting Evaluation Costs: An Investigation into Early Termination in Genetic Programming
    Namyong Park, Kangil Kim, and R.I. (Bob) McKay
    IEEE Congress on Evolutionary Computation (CEC) 2013.
    [paper]
  • Evolving the Best Known Approximation to the Q Function
    Dao Ngoc Phon, Nguyen Xuan Hoai, R. I. (Bob) McKay, Constantin Siriteanu, Nguyen Quang Uy, and Namyong Park
    The 2012 Genetic and Evolutionary Computation Conference (GECCO) 2012.
    [paper]
Education
  • Ph.D. in Computer Science, Carnegie Mellon University, Sep 2017 – Present
  • M.S. in Electrical Engineering and Computer Science, Seoul National University, Aug 2013
  • B.S. in Computer Science and Engineering, Seoul National University, Aug 2010
Work & Research Experience
  • Carnegie Mellon University, Pittsburgh, PA
    Research Assistant • Sep 2017 – Present
  • Microsoft Research, Redmond, WA
    Research Intern • June 2020 – Aug 2020
  • Amazon, Seattle, WA
    Applied Scientist Intern • May 2019 – Aug 2019, May 2018 – Aug 2018
  • Seoul National University, Seoul, Korea
    Research Staff • Data Mining Lab • Sep 2015 – Jul 2017
  • SAP Labs Korea, Seoul, Korea
    Software Developer • Jan 2015 – Jan 2016
  • Fancy, New York, NY, USA (remote)
    Software Engineer • Aug 2013 – Dec 2014
  • Seoul National University, Seoul, Korea
    Research Assistant • Structural Complexity Lab • Feb 2011 – Jul 2013
  • NHN, Seongnam, Korea
    Software Engineer • Feb 2006 – Aug 2008