Noseong Park


Scientific knowledge, written in the form of differential equations, plays a vital role in various deep learning fields. In this talk, I will present a graph neural network (GNN) design based on reaction-diffusion equations, which addresses the notorious oversmoothing problem of GNNs. Since the self-attention of Transformers can also be viewed as a special case of graph processing, I will present how we can enhance Transformers in a similar way. I will also introduce a spatiotemporal forecasting model based on neural controlled differential equations (NCDEs). NCDEs were designed to process irregular time series in a continuous manner and for spatiotemporal processing, it needs to be combined with a spatial processing module, i.e., GNN. I will show how this can be done. 

Our speaker

Noseong Park is currently a tenured associate professor of the School of Computing and the director of the Big Data Analytics and Learning Laboratory at Korea Advanced Institute of Science and Technology (KAIST) in South Korea. Before joining KAIST, he was a tenured associate professor at Yonsei University from 2020 to 2024 and was an assistant professor at George Mason University from 2018 to 2019 and the University of North Carolina at Charlotte from 2016 to 2018. He received his computer science Ph.D. in 2016 from the University of Maryland, College Park under the supervision of Prof. V. S. Subrahmanian. He received his Master's degree from KAIST and Bachelor's degree from Soongsil University. He has much experience on the core fields of data mining and machine learning, and their applications to scientific machine learning, deep generative learning, spatiotemporal processing, time series processing, recommender systems, etc. He has published more than 50 papers in top-tier venues, such as NeurIPS, ICLR, ICML, KDD, WWW, VLDB, ICDM, WSDM, SIGIR, IJCAI, AAAI and so on. In particular, his VLDB paper, which proposed a tabular data synthesis method called TableGAN, is now the most cited among all VLDB papers published within the last 5 years. He received the best student paper award, as an advisor, from IEEE BigData 2022, the best demonstration runner-up award from IJCAI 2019, and the best paper runner-up award from ASONAM 2016. He also received a couple of medals in Samsung Humantech Paper Award, the most prestigious award in Korea, at 2022 and 2023. He has also secured several research grants that amount to, in total, $530K as a PI or co-PI from NSF, the Office of Naval Research, and so forth when he was in the US. Including the grants that he has received in Korea after 2020, the total research fund amounts to $2M. He also has served (or is now serving) as a PC member in many top-tier venues, such as ICML, ICLR, NeurIPS, KDD, WWW, AAAI, ICDM, ICWSM, and so on.