Professor  Hanghang Tong

Hanghang Tong is currently a professor & university scholar (class 2024) at the Siebel School of Computing and Data Science (the previous Department of Computer Science) at University of Illinois at Urbana-Champaign. Before that, he worked at Arizona State University as an associate professor, at City University of New York (City College) as an assistant professor and at IBM T. J. Watson Research Center as a Research Staff Member. He received his Ph.D. from the Machine Learning Department of School of Computer Science at Carnegie Mellon University in 2009. His major research interest lies in large-scale data mining, machine learning and AI for graphs and multimedia. In the past, he has published 300+ papers at these areas and his research has received several awards, including SDM/IBM 2018 early career data mining research award, two ‘test of time’ awards (ICDM 2015 & 2022 10-Year Highest Impact Paper award), ICDM Tao Li award (2019), NSF CAREER award, and several best paper awards (e.g., ICDM’06 best paper, SDM’08 best paper, CIKM’12 best paper, etc.). He was Editor-in-Chief of ACM SIGKDD Explorations (2018-2022) and is currently serving co-Editor-in-Chief of ACM Computing Surveys (CSUR). He is a senior member of AAAI (2024), a fellow of IEEE (2022), and a Fellow of ACM (2025).

• Graph Neural Networks Beyond Homophily
  The emergence of deep learning models designed for graph and network data, often under an umbrella term named graph neural networks (GNNs for short), has largely streamlined many graph learning...
• Optimal Deep Graph Learning: Towards a New Frontier
  The emergence of deep learning models designed for graph and network data, often under an umbrella term named graph neural networks (GNNs for short), has largely streamlined many graph learning...