Sungwon Han (한성원)

I'm a Ph.D. candidate researcher in Data Science Group at KAIST, South Korea.
My research interests include developing a robust representation learning algorithms
to deal with the data deficiency and corruption, which are common in publicly available datasets.

Publications

S. Park*, S. Han*, S. Kim, D. Kim, S. Park, S. Hong, M. Cha. Improving Unsupervised Image Clustering With Robust Learning, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021

S. Park*, S. Han*, J. Kim*, M. Molaie, H. Vu, K. Singh, J. Han, W. Lee, M. Cha. COVID-19 discourse on Twitter: Case study of risk communication in four Asian countries, Journal of Medical Internet Research (JMIR), 2021

S. Kim, T. Mai, T. Nguyen, S. Han, S. Park, K. Singh, M. Cha. Take a Chance: Managing the Exploitation-Exploration Dilemma in Customs Fraud Detection via Online Active Learning, ArXiv preprint, 2020

D. Ahn, M. Cha, S. Han, J. Kim, S. Lee, S. Park, S. Park, H. Yang, J. Yang. Teaching Machines to Measure Economic Activities from Satellite Images: Challenges and Solutions, Banca d'Italia and FRB Joint Conference, 2020

S. Park, D. Ahn, S. Han, E. Lee, D. Kim, J. Yang, S. Lee, S. Park, H. Yang, J. Kim, M. Cha. Human-in-the-loop solution for scoring economic development from geospatial data, NeurIPS Workshop HAMLETS, 2020

S. Han, S. Park, S. Park, S. Kim, M. Cha. Mitigating Embedding and Class Assignment Mismatch in Unsupervised Image Classification, In proc. of the European Conference on Computer Vision (ECCV), 2020

Y. Xu*, S. Han, S. Park, M. Cha, CT. LI. A Comprehensive Approach to Unsupervised Embedding Learning based on AND Algorithm, the IEEE International Conference on Big Data, 2020 (Acceptance Rate=15.5\%)

S. Han, D. Ahn, S. Park, J. Yang, S. Lee, J. Kim, H. Yang, S. Park, and M. Cha. Learning to Score Economic Development from Satellite Imagery, In proc. of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020 (Acceptance Rate=16\%)

S. Han, D. Ahn, H. Cha, J. Yang, S. Park, and M. Cha. Lightweight and robust representation of economic scales from satellite imagery, Association for the Advancement of Artificial Intelligence (AAAI) a special track on artificial intelligence for social impact (AISI), 2020

C. Phentmunee, H. Doan Thi, H. Dieu Vu, D. Ahn, H.Cha, S. Han, and M. Cha. Image Super Resolution Techniques Applied on Satellite Imagery, International Conference on Computer Vision (ICCV) Workshop Real-World Recognition from Low-Quality Images and Videos (RLQ) track, 2019

D. Ahn, S. Han, H. Cha, and M. Cha. Predicting Urbanization from Daytime Satellite Imagery based on Descriptive Statistics, International Joint Conferences on Artificial Intelligence (IJCAI) Workshop, 2019

S. Park, S.-W. Lee, S. Han, and M. Cha. Clustering Insomnia Patterns by Data from Wearable Devices: Algorithm Development and Validation, In JMIR mHealth and uHealth (JMU), 2019. (SCI-E, IF=4.3)

S. Park, C-T. Li, S. Han, C. Hsu, S.W. Lee, and M. Cha. Learning Sleep Quality from Daily Logs, In proc. of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019.

S. Han, S. W. Lee, S. Park, and M. Cha. Towards precision psychiatry: Unsupervised clustering of insomnia patterns with a convolutional autoencoder, In proc. of the Korean Software Congress (KSC), 2018. (Korean)

S. Park, S. W. Lee, S. Han, and M. Cha. Exploring Insomnia-related Clusters based on Intricate Relationship Among Behavioral, Biological, and Sleeping Data: Focusing on a Smart Band Wearing Experiment, In proc. of the Korean DataBase Conference (KDBC), 2018. (Korean)