Tong’s Early Career Achievements Recognized With IEEE ICDM Tao Li Award
Illinois Computer Science Associate Professor Hanghang Tong was honored with the prestigious Tao Li Award by the IEEE International Conference on Data Mining (ICDM) at its recent 2019 conference for his “contributions to large-scale graph mining.”
The Tao Li Award recognizes excellent early career researchers in the areas of data mining, machine learning, and artificial intelligence who have demonstrated significant impact through research contributions, leadership, and service. Tong is the second recipient of the award, which was established in 2017 through public donations to remember the life and accomplishments of Professor Tao Li.
Tong studies large-scale data mining and machine learning for graph and multimedia data, with applications in social network analysis, healthcare, cyber-security, civil engineering and e-commerce. His research contributions have included an improved method for random walk with restart, which is a relevance score for two nodes in a weighted graph. His algorithm showed up to a 150x speed up while preserving the quality of results. This work received a best paper award in 2006, and it was recognized in 2015 with IEEE ICDM’s 10-Year Highest Impact Paper Award.
An author of more than 100 publications and refereed articles, Tong’s other awards include the SDM/IBM Early Career Data Mining Research Award (2018), National Science Foundation CAREER Award (2017), and several best paper and demo awards. Tong also is the editor-in-chief of SIGKDD Explorations, an action editor of Data Mining and Knowledge Discovery (Springer), and an associate editor of Knowledge and Information Systems (Springer) and Neurocomputing Journal (Elsevier).
About IEEE ICDM
The IEEE International Conference on Data Mining (ICDM) has established itself as the world’s premier research conference in data mining. It provides an international forum for presentation of original research results, as well as exchange and dissemination of innovative and practical development experiences. By promoting novel, high-quality research findings, and innovative solutions to challenging data mining problems, the conference seeks to advance the state-of-the-art in data mining.