معرفی
Hanghang Tong is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), holding the title of University Scholar. He specializes in large-scale data mining, machine learning, and AI with a focus on graph and multimedia data. His research addresses applications in social networks, healthcare, cybersecurity, and cyber-physical systems.
Education: PhD and M.Sc. in Machine Learning from Carnegie Mellon University (2008–2009). Prior to UIUC, he was an Associate Professor at Arizona State University.
Research: Leads the IDEA Lab, focusing on network robustification, fair network learning, and multi-network alignment. Key areas include optimizing graph connectivity, algorithmic fairness, and developing tools like FASTEN and Sylvester Equation solvers.
Awards: IEEE Fellow (2021), NSF CAREER Award (2017), multiple best paper awards, and the ICDM 10-Year Highest Impact Paper Award (2015). Editor-in-Chief of ACM SIGKDD Explorations and associate editor of ACM Computing Surveys.
Teaching: Courses include CS512 (Data Mining Principles), CS514 (Advanced Network Science), and CS412 (Introduction to Data Mining). Authored influential textbooks like Data Mining: Concepts and Techniques (4th ed., 2022).
Service: Organized key conferences (CIKM, DSAA) and workshops on adversarial activity modeling. Active in academic leadership and editorial roles.
Labs/Teams: Runs the IDEA Lab, collaborating on projects like NetFair (fair network learning) and Network Correspondence Mining. Supervises a vibrant group of PhD and Master’s students in graph algorithms and data science.
