Intelligence and Optimization in Networks Lab

Recruiting & Contact

We are looking for motivated graduate students and interns to work with us.

Interested in joining the ION lab? Please feel free to contact us with your CV if you find our research interests well-matched with yours and if you would like to know more about collaboration opportunities. 

ION 연구실에 관심이 있다면 다음의 글과 연구실 소개자료도 확인해보세요! 

ION 연구실에서는 함께 연구를 수행하고자 하는 학석사연계과정 희망생 및 학부연구생 을 환영합니다. 연구실에 참여하고 싶은 분은 PI에게 연락하여 면담을 신청하기 바랍니다.  (2025년도 전반기 (겨울방학+1학기) 희망자 recruit)

• PI와 사전 면담 후 진행 

Lab News

[2025.01] Junseo's paper "I2AM: Interpreting Image-to-Image Latent Diffusion Models via Bi-Attribution Maps" has been accepted to ICLR 2025 (AI top conference)! Congratulations! [news]

[2024.10] Xinyu's paper "Multi-adversarial Autoencoders: Stable, Faster and Self-adaptive Representation Learning" has been accepted to Expert Systems with Applications (JCR top 10% journal). Congratulations!

[2024.09] Jiyong's paper "SimFLE: Simple Facial Landmark Encoding for Self-Supervised Facial Expression Recognition in the Wild" has been accepted to IEEE Tran. on Affective Computing (JCR top 5% journal). Congratulations!

[2024.10] Jungui, Yeryung, Chaeyeon's paper and Dongkyung, Jaeyeon's paper have been accepted to KAIC 2024. Two papers won the Outstanding Paper Award (장려상) at KAIC. Congratulations!

[2024.09] Jeongmin's paper "Data Augmentation Techniques using Text-to-Image Diffusion Models for Enhanced Data Diversity" has been accepted to ICTC 2024. Congratulations!

[2024.08] Minji, Jaeyeon and Yeryung join the group as BS/MS Integrated students! :)

[2024.06] Junseo's paper has been accepted to XAI workshop on IJCAI 2024 (AI top conference)! Congratulations!

[2024.02] Hanyoung Roh joins the group as a graduate student. Welcome, Hanyoung :)

Research Areas

• Machine Learning (기계학습)

• Learning and Inference based on Graphical Models (그래프 기반 학습 및 추론)

• Generative Models (생성 모델)

• Intelligent Networks (지능형 네트워크)

Selected Publications

• I2AM: Interpreting Image-to-Image Latent Diffusion Models via Bi-Attribution Maps, ICLR 2025 (AI top-conference)

• Sound-based Sleep Staging by Exploiting Real-world Unlabeled Data, ICLR workshop 2023 

• Multi-Sample Online Learning for Probabilistic Spiking Neural Networks, IEEE TNNLS 2022 (JCR Q1, Top 1%)

• On Cost-efficient Learning of Data Dependency, IEEE TNET 2022 (JCR Q1, Top 10%)

• Learning to Time-Decode in Spiking Neural Networks Through the Information Bottleneck, NeurIPS 2021 (AI top-conference)

• Solving Continual Combinatorial Selection via Deep Reinforcement Learning, IJCAI 2019 (AI top-conference)

• An Introduction to Probabilistic Spiking Neural Networks, IEEE SPM 2019 (JCR Q1, Top 2.5%)