Weijing Tang

Weijing Tang is an Assistant Professor in the Department of Statistics & Data Science at Carnegie Mellon University (CMU), affiliated with the Dietrich College of Humanities and Social Sciences. His research focuses on developing statistical methodologies for network analysis, machine learning, and survival analysis, addressing challenges in large-scale interdisciplinary datasets across health and social sciences. He holds a Ph.D. in Statistics from the University of Michigan (2022) and a B.S. in Mathematics from Tsinghua University (2016). Prior to CMU, he was a Postdoctoral Research Fellow in Biostatistics at Harvard University (2022-2023).

Education:

• Ph.D. in Statistics, University of Michigan, 2022
• B.S. in Mathematics, Tsinghua University, 2016

Research Interests:

• Network analysis and signed network dynamics
• Survival analysis and Cox models
• Machine learning applications in biostatistics and public policy
• Optimization and scalable statistical methods
• Nonparametric methods and subgroup heterogeneity analysis

Key Awards:

• ProQuest Distinguished Dissertation Award (2023)
• IMS Hannan Graduate Student Travel Award (2022)
• Multiple awards from ASA and ENAR (2021-2020)
• Grand Prize in American Heart Association's COVID-19 Data Challenge (2020)

Funding:

• NSF Grant DMS-2412853 (sole PI)
• CMU Dietrich College Seed Grant (co-PI)