Lab. of Advanced Application for Intelligence Systems at Hanyang University

We perform principled research for learning and making intelligence systems. Our interest includes machine learning, artificial intelligence, data science, and mathematical psychology. We consider these disparate fields, individually and in combination, as advanced applications of mathematical principles and computational theory.

The Lab. of advanced application for intelligence systems (AAIS, http://aais.hanyang.ac.kr) at Hanyang University started in 2019, and we are recruiting graduate students and postdocs who are motivated to understand the state-of-the-art deep learning and its applications in diverse real world problems. More information is as follows:

Current lab. members

  1. Yung-Kyun Noh (Principal Inverstigator) link
  2. Byeong-Gul Choi (Post-doc)
  3. Sang-Kyun Ko (MS. student)

Current projects

  1. Development of probability-density-based nearest neighbor prediction algorithms in extremely large data analysis (Samsung Research Funding and Incubabion Center for Future Technology, 2019.6. - 2021.5.)
  2. Research of nonparametric methods for the estimation of information-theoretic measures (National Research Foundation of Korea, 2017.9. - 2020.8.)

Selected recent papers

  1. Noh, Y-K., Park, J. Y., Choi, B. G., Kim, K.-E. and Rha, S.-W. (2019) A Machine Learning-Based Approach for the Prediction of Acute Coronary Syndrome Requiring Revascularization, Journal of Medical Systems, 43:253 (in press)
  2. Noh, Y.-K., Sugiyama, M., Liu, S., Marthinus, C., Park, F. C., and Lee, D. D. (2018), Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence, Neural Computation, 30(7):1930-1960
  3. Noh, Y.-K., Hamm, J., Park, F. C., Zhang, B.-T., and Lee, D. D. (2018) Fluid Dynamic Models for Bhattacharyya-based Discriminant Analysis, IEEE Transactions in Pattern Analysis and Machine Intelligence, 40(1):92-105
  4. Noh, Y.-K., Zhang, B.-T., and Lee, D. D. (2018), Generative Local Metric Learning for Nearest Neighbor Classification, IEEE Transactions in Pattern Analysis and Machine Intelligence, 40(1):106-118
  5. Noh, Y.-K., Sugiyama, M., Kim, K.-E., Park, F. C., and Lee, D. D. (2017), Generative Local Metric Learning for Kernel Regression, Advances in Neural Information Processing Systems 30

Active Collaborators

Sangwoong Yoon (SNU robotics), Eun-Sol Kim (Kakao brain), Jiseob Kim (SNU), Daniel D. Lee (Cornell Tech/Samsung research), Young-Han Kim (USCD/SK hynix), Masashi Sugiyama (U. Tokyo/RIKEN AIP), Jay Myung (OSU), Mark Pitt (OSU), Kee-Eung Kim (KAIST), Frank C. Park (SNU), Seung-Woon Rha (Korea University), Ji-Young Park (Eulji University), Sun Kim (SNU), Sungroh Yoon (SNU), Yeon-Hee Lee (Kyung Hee University), Seunghyun Kim (SNU robotics), Gunsu Yun (POSTECH)