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, 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

  • December 2024, I have received the Outstanding Teaching Awards from Sogang University.
  • August 2024, I have received the Global Basic Research Grant for "Developing a High-Performance Computing-Data Platform for Accelerating Large-Scale Machine Learning." (National Research Foundation of Korea, Co-PI, 2024.08 ~ 2027.07)
  • April 2024, I have received the Outstanding Young Scientist Grant for "Optimizing Distributed Deep Learning and Federated Learning for Accelerating Large-Scale Deep Learning Models." (National Research Foundation of Korea, PI, ₩800M, 2024.04 ~ 2027.03)
  • September 2022, I started as an Assistant Professor at Sogang University.
  • June 2022, I am serving on the Program Committee at the 40th IEEE International Conference on Computer Design 2022.
  • January 2022, Our paper on Parallel Training of GRU Networks with a Multi-Grid Solver for Long Sequences was accepted to ICLR 2022!

Selected Publications

  • Sejeong Oh, Gordon E. Moon, and Sungyong Park, "ML-based Dynamic Operator-Level Query Mapping for Stream Processing Systems in Heterogeneous Computing Environments," Proceedings of the IEEE International Conference on Cluster Computing (CLUSTER'24) [paper]
  • Eunji Lee, Yoonsang Han, and Gordon E. Moon, "Accelerated Block-Sparsity-Aware Matrix Reordering for Leveraging Tensor Cores in Sparse Matrix-Multivector Multiplication," Proceedings of the 30th International European Conference on Parallel and Distributed Computing (Euro-Par'24) [paper]
  • Bokyeong Yoon, Yoonsang Han, and Gordon E. Moon, "Layer-Wise Sparse Training of Transformer via Convolutional Flood Filling," Proceedings of the 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'24) [paper]
  • 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]
  • 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) [paper]

Teaching

  • Introduction to Computer Systems (2024 Spring, 2023 Spring)
  • AI Programming (2024 Spring, 2023 Spring, 2022 Spring, 2021 Spring)
  • Operating Systems (2023 Autumn, 2022 Autumn)
  • Advanced GPU Programming (2024 Autumn, 2023 Autumn)
  • Computer Programming II (2022 Autumn)
  • Recommender Systems (2024 Autumn, 2022 Spring)
  • Parallel Computing (2021 Autumn)
  • System Programming (2021 Autumn)