IISL@HYU

Information and Intelligence Systems Lab (IISL) is a research group in the Department of Electronic Engineering (and affiliated with the Graduate School of Aritificial Intelligence Semiconductor) at Hanyang University. Our research focuses on artificial intelligence, optimizations, statistical inference, information theory and a broad range of applications. Core topics include (but are not limited to) faster learning for hyperscale AI, distributed learning, reinforcement learning, AI for wireless communications, large-scale optimization, and statistical signal processing.

Opening

We are actively looking for motivated graduate students who are interested in machine learning, reinforcement learning, wireless communications, or optimizations.
- Openings for AI Algorithms/Optimizations and Reinforcement Learning in the Graduate School of Artificial Intelligence Semiconductor (인공지능반도체 대학원)
- Openings for Machine Learning (Continual Learning/Federated Learning/Many-Task Learning) and 6G Communications in the Department of Electronic Engineering (BK21, Beyond-G Global Innovation Center)

There are 1~2 openings for Fall-2026. If interested, please contact me via email

News

[November 2025] H. Joo, S. Hong, and D.-J. Shin, "FedLSC: Improving Communication Efficiency and Robustness in Federated Learning with Stragglers and Adversaries," IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 11, pp. 19805-19819, Nov. 2025.
[October 2025] J. Lee and S. Hong, "Piecewise Beam Training and Channel Estimation for RIS-Aided Near-Field Communications," accepted to IEEE Transactions on Wireless Communications.
[September 2025] S.-W. Hong, Y. Jeong, U. Hwang, and S. Hong, "QIPPO/CA: A Quantized Communication-Efficient MARL Framework for Fully Distributed Channel Access in Next-Generation Wireless Networks," submitted to IEEE Internet of Things Journal (under minor revision).
[September 2025] D. Kwon, Y. Jeong, S.-W. Hong, and S. Hong, "Federated Model-Based Offline Multi-Agent Reinforcement Learning for Wireless Networks," NeurIPS Workshops on AI4NextG, 2025.
[September 2025] J. Lee and S. Hong, "Blind Massive MIMO for Dense IoT Networks," IEEE Internet of Things Journal, vol. 12, no. 17, pp. 35678-35691, Sep. 2025.
[July 2025] U. Hwang and S. Hong, "Federated Reinforcement Learning in Heterogeneous Environments," submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence.
[June 2025] J. Lee and S. Hong, "Near-field LoS/NLoS channel estimation for RIS-Aided MU-MIMO systems: piece-wise low-rank approximation approach," IEEE Transactions on Wireless Communications, vol. 24, no. 6, pp. 4781-4792, Jun. 2025.
[May 2025] S. Choi, J. Lee, S. Lim, H. Park, S. Hong and J. Han, "REACT: Recursive Algorithm for RC Network Transfer Functions," submitted to IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
[April 2025] U. Hwang and S. Hong, "On Practical Robust Reinforcement Learning: Adjacent Uncertainty Set and Double-Agent Algorithm," IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 4, pp. 7696-7710, Apr. 2025.

Repositories

IISL github repository: click here
IISL presentation repository: click here