As a doctoral student at KAIST in South Korea, I am passionate about practical machine learning and its real-world applications. My research has been recognized and published in top conferences such as AAAI, NeurIPS, and KDD. Some of my notable publications include the below:

• Kim, D., Min, H., Nam, Y., Song, H., Yoon, S., Kim, M., and Lee, J., "COVID-EENet: Predicting Fine-Grained Impact of COVID-19 on Local Economies," In Proc. 36th AAAI Conf. on Artificial Intelligence (AAAI), 2022 (top conference, oral paper)
• Park, D., Choi, S., Kim, D., Song, H., and Lee, J., "Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy," In Proc. 37th Annual Conference on Neural Information Processing Systems (NeurIPS), 2023 (top conference, acceptance rate: 25.6%).
• Kim, M., Song, H., Kim, D., Shin, K., and Lee, J., "PREMERE: Meta-Reweighting via Self-Ensembling for Point-of-Interest Recommendation," In Proc. 35th AAAI Conf. on Artificial Intelligence (AAAI), 2021 (top conference)
• Kim, M., Kang, J., Kim, D., Song, H., Min, H., Nam, Y., Park, D., and Lee, J., "Hi-COVIDNet: Deep Learning Approach to Predict Inbound COVID-19 Patients and Case Study in South Korea," In Proc. 26th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD), 2020 (top conference, AI for COVID track).