Noseong Park

Noseong Park is a tenured associate professor in the School of Computing at KAIST, where he also serves as the director of the Big Data Analytics and Learning Laboratory. His research focuses on data mining and machine learning, with applications in scientific machine learning, deep generative learning, spatiotemporal processing, time series processing, and recommender systems. He has published over 70 papers in top-tier venues, including NeurIPS, ICLR, ICML, KDD, WWW, and VLDB. His work on TableGAN, a method for tabular data synthesis, is the most cited VLDB paper in the last six years. He has also served as an area chair or program committee member for numerous conferences, including ICML, ICLR, NeurIPS, and KDD, and will serve as general chair for CIKM in 2025.

Noseong Park is a Tenured Associate Professor at KAIST, specializing in Artificial Intelligence. His research focuses on developing advanced machine learning techniques for data synthesis, time series forecasting, and network analysis. Park's work includes generative adversarial networks (GANs) for creating synthetic data, graph neural networks for traffic prediction, and physics-informed neural networks for solving partial differential equations. He has also explored applications in cybersecurity, such as phishing URL detection using text generative adversarial networks. His research emphasizes the integration of deep learning with domain-specific knowledge to enhance model performance and generalization.