Namyong Park

PhD Candidate in the Computer Science Department at Carnegie Mellon University
Email: namyongp at cs.cmu.edu

About

I am a PhD Candicate in the Computer Science Department at Carnegie Mellon University, where I am advised by Prof. Christos Faloutsos. During my PhD studies, I’ve also spent time at Adobe Research (2021), Microsoft Research (2020), and Amazon (2019, 2018) as a research intern. My PhD studies are supported by the Bloomberg Data Science PhD Fellowship and the ILJU Foundation PhD Fellowship.

Research Interests

Graph Machine Learning, Dynamic Networks, Knowledge Graphs, Anomaly Detection, Explainable AI.

Publications

  1. 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 (to appear)
    PDF
  2. TheWebConf
    VisPaD: Visualization and Pattern Discovery for Fighting Human Trafficking
    Pratheeksha Nair, Yifei Li, Catalina Vajiac, Andreas Olligschlaeger, Meng-Chieh Lee, Namyong Park, Duen Horng Chau, Christos Faloutsos, and Reihaneh Rabbany
    The ACM Web Conference (TheWebConf) 2022 (to appear)
    Code Data
  3. 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 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
    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. AAAI-EDU
    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
    PDF
  5. AAAI-EDU
    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
    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
  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, Yon Su Kim, Hyung Jin Yoon, and Hajeong Lee
    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
    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
  1. GECCO
    Evolving the Best Known Approximation to the Q Function
    Dao Ngoc Phong, Nguyen Xuan Hoai, Robert Ian (Bob) McKay, Constantin Siriteanu, Nguyen Quang Uy, and Namyong Park
    Genetic and Evolutionary Computation Conference (GECCO) 2012
    PDF

Education

Work & Research Experience

  • Adobe Research, San Jose, CA
    Research Intern • May 2021 – Aug 2021
  • 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 • Jan 2016 – 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