Gordon Euhyun Moon

Gordon Euhyun Moon (문의현)

Assistant Professor
Department of Computer Science and Engineering
College of Engineering
Sogang University
Office: Adam Schall Hall, Room 813
Email: ehmoon (at) sogang.ac.kr


I am an Assistant Professor in the department of Computer Science and Engineering at Sogang University. My research interests involve connecting the areas of Machine Learning and High Performance Computing—more specifically, parallelization of Deep Neural Networks. I am also interested in applications in recommender systems, healthcare, activity recognition, natural language processing, and general AI.
Before joining Sogang, I received my Ph.D. from The Ohio State University, did a postdoctoral researcher in the Center for Computing Research at Sandia National Laboratories, and was an Assistant Professor in the department of Software at Korea Aerospace University.

I have interest in advising undergraduate and graduate students. Please contact me if you are passionate about research related to ML and HPC.


Recent News

Publications

  • Bokyeong Yoon, Yoonsang Han, and Gordon E. Moon, "Layer-Wise Sparse Training of Transformer via Convolutional Flood Filling," To Appear in Proceedings of the 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'24)
  • Sanha Maeng, Gordon E. Moon, and Sungyong Park, "Chronica: A Data-Imbalance-Aware Scheduler for Distributed Deep Learning," Proceedings of the 23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid'23) [paper]
  • Gordon E. Moon, and Eric C. Cyr, "Parallel Training of GRU Networks with a Multi-Grid Solver for Long Sequences," Proceedings of the 10th International Conference on Learning Representations (ICLR'22) [paper][slides][preprint-pdf]
  • Gordon E. Moon, Hyoukjun Kwon, Geonhwa Jeong, Prasanth Chatarasi, Sivasankaran Rajamanickam, and Tushar Krishna, "Evaluating Spatial Accelerator Architectures with Tiled Matrix-Matrix Multiplication," IEEE Transactions on Parallel and Distributed Systems (TPDS'22) [paper][preprint-pdf]
  • Eric Qin, Geonhwa Jeong, William Won, Sheng-Chun Kao, Hyoukjun Kwon, Sudarshan Srinivasan, Dipankar Das, Gordon E. Moon, Sivasankaran Rajamanickam, and Tushar Krishna, "Extending Sparse Tensor Accelerators to Support Multiple Compression Formats," Proceedings of the 35th IEEE International Parallel & Distributed Processing Symposium (IPDPS'21) [paper][preprint-pdf]
  • Gordon E. Moon, J. Austin Ellis, Aravind Sukumaran-Rajam, Srinivasan Parthasarathy, and P. Sadayappan, "ALO-NMF: Accelerated Locality-Optimized Non-negative Matrix Factorization," Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2020 (KDD'20, acceptance rate: ~16.8%, research track, oral and poster presentations) [pdf][code]
  • Gordon E. Moon, Denis Newman-Griffis, Jinsung Kim, Aravind Sukumaran-Rajam, Eric Fosler-Lussier, and P. Sadayappan, "Parallel Data-Local Training for Optimizing Word2Vec Embeddings for Word and Graph Embeddings," Proceedings of the 5th International Workshop on Machine Learning in High-Performance Computing Environments (MLHPC 2019), held in conjunction with International Conference for High Performance Computing, Networking, Storage, and Analysis (SC'19) [pdf][code]
  • Gordon E. Moon, Israt Nisa, Aravind Sukumaran-Rajam, Bortik Bandyopadhyay, Srinivasan Parthasarathy, and P. Sadayappan, "Parallel Latent Dirichlet Allocation on GPUs," Proceedings of the 2018 International Conference on Computational Science (ICCS'18) [pdf]
  • Gordon E. Moon, Aravind Sukumaran-Rajam, and P. Sadayappan, "Parallel LDA with Over-Decomposition," Proceedings of the 2017 IEEE 24th International Conference on High Performance Computing Workshops (HiPCW'17) [pdf]
  • Gordon E. Moon and Jihun Hamm, "A Large-Scale Study in Predictability of Daily Activities and Places," Proceedings of the 8th EAI International Conference on Mobile Computing, Applications and Services (MobiCASE'16) [pdf]

Teaching

  • Operating Systems (2023 Autumn)
  • Advanced GPU Programming (2023 Autumn)
  • Introduction to Computer Systems (2023 Spring)
  • AI Programming (2023 Spring)
  • Computer Programming II (2022 Autumn)
  • Operating Systems (2022 Autumn)
  • AI Programming (2022 Spring)
  • Recommender Systems (2022 Spring)
  • AI Programming (2021 Spring)
  • Parallel Computing (2021 Autumn)
  • System Programming (2021 Autumn)