We are a research group headed by Prof. Jaekyun Moon in the School of EE at KAIST. We work on distributed and decentralized forms of machine learning, storage and communications, dealing with optimal ways to store, access and process big data in today's densely connected world. Distributed and decentralized ways of data storage and machine learning are essential in the era of IoT, big data and connected AI.

About & Contact

We are looking for integrated MS/PhD candidates in areas of machine learning (with emphasis on distributed learning, low-data learning and low-complexity learning) and distributed storage. Interested candidates may contact Prof. Jaekyun Moon via email (

Research Areas: distributed/decentralized machine learning, learning on low data, and hardware-friendly learning algorithms, all using various theoretical tools and insights from the fields of communications, signal processing, statistics and information theory.


2022.04 Two papers accepted to CVPR Workshop:
  • "Training Multi-Exit Architectures via Block-Dependent Losses for Anytime Inference"
  • "Active Object Detection with Epistemic Uncertainty and Hierarchical Information Aggregation"
  • 2022.04 D.-J. Han received the Best Ph.D. Dissertation Award from KAIST EE
    2021.10 Y. Park's paper entitled "CAFENet: Class-Agnostic Few-Shot Edge Detection Network" accepted to BMVC 2021
    2021.09 Two papers accepted to NeurIPS 2021:
  • "Few-Round Learning for Federated Learning"
  • "Sageflow: Robust Federated Learning against Both Stragglers and Adversaries"
  • 2021.08 D.-J. Han's paper entitled "FedMes: Speeding Up Federated Learning with Multiple Edge Servers" accepted for publication in IEEE Journal on Selected Areas in Communications
    2021.07 Two papers accepted to ICML Workshop:
  • "Accelerating Federated Learning with Split Learning on Locally Generated Losses"
  • "Handling Both Stragglers and Adversaries for Robust Federated Learning"
  • 2021.06 D.-J. Han's paper entitled "Coded Wireless Distributed Computing with Packet Losses and Retransmissions" accepted for publication in IEEE Transactions on Wireless Communications
    2020.12 D.-J. Han's paper entitled "TiBroco: A Fast and Secure Distributed Learning Framework for Tiered Wireless Edge Networks" accepted to IEEE INFOCOM 2021
    2020.12 M. Choi's paper entitled "Probabilistic Caching and Dynamic Delivery Policies for Categorized Contents and Consecutive User Demands" accepted for publication in IEEE Transactions on Wireless Communications
    2020.11 D.-J. Han's paper entitled "Hierarchical Broadcast Coding: Expediting Distributed Learning at the Wireless Edge" accepted for publication in IEEE Transactions on Wireless Communications
    2020.10 S. Park's paper entitled "Characterization of Inter-Cell Interference in 3D NAND Flash Memory" accepted for publication in IEEE Transactions Circuits and Systems I: Regular Papers
    2020.09 J. Sohn's paper entitled "Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks" accepted to Neural Information Processing Systems (NeurIPS) 2020
    2020.07 J. Sohn's paper entitled "GAN-mixup: Augmenting Across Data Manifolds for Improved Robustness" accepted to ICML Workshop on Uncertainty & Robustness in Deep Learning
    2020.06 S. W. Yoon and D.-Y. Kim's paper entitled "XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning" accepted to International Conference on Machine Learning (ICML) 2020
    2020.03 Dr. Sung Whan Yoon, a Ph.D. alumnus of our group joined UNIST as an Assistant Professor
    2020.03 Dr. Minseok Choi, a Ph.D. alumnus of our group joined Jeju National University as an Assistant Professor
    2020.03 So Yeong Kim and Jinho Kim joined our lab. Welcome!
    2019.07 M. Choi's paper entitled “Dynamic Power Allocation and User Scheduling for Power-Efficient and Delay-Constrained Multiple Access Networks” accepted for publication in IEEE Transactions on Wireless Communications
    2019.06 B. Choi's paper entitled "Secure Clustered Distributed Storage Against Eavesdropping" accepted for publication in IEEE Transactions on Information Theory
    2019.04 S. W. Yoon and J. Seo's paper entitled "TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning" accepted to the 36th International Conference on Machine Learning (ICML) 2019
    2019.04 Four papers accepted in IEEE International Symposium on Information Theory (ISIT) 2019
  • H. Park and J. Moon, "Irregular Product Coded Computation for High-Dimensional Matrix Multiplication"
  • B. Choi, J. Sohn, D.-J. Han and J. Moon, "Scalable Network-Coded PBFT Consensus Algorithm"
  • D.-J. Han, J. Sohn and J. Moon, "Coded Distributed Computing over Packet Erasure Channels"
  • M. Kim, J. Sohn and J. Moon, "Coded Matrix Multiplication on a Group-Based Model"
  • Copyright © Moon Lab., 2017
    School of Electrical Engineering, Korea Advanced Institute of Science and Technology
    291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea