Tian Zheng
Scholar

Tian Zheng

Google Scholar ID: -4J-KZoAAAAJ
Columbia University
Statistical LearningData ScienceStatistical GeneticsSocial Network AnalysisSpatiotemporal
Citations & Impact
All-time
Citations
2,308
 
H-index
19
 
i10-index
41
 
Publications
20
 
Co-authors
9
list available
Resume (English only)
Academic Achievements
  • Serves on the National Academies Committee on the Frontiers of Statistics in Science and Engineering: 2035 and Beyond
  • Founded the aiX Faculty Fellowship Program and AI+X Faculty Leadership Fellows Program to advance discipline-specific AI education
  • Published reflections on structuring AI education dialogues on arXiv
  • Published an article on the Collaboratory model in Harvard Data Science Review
  • 2025 preprint: 'AI Education in Higher Education: A Taxonomy for Curriculum Reform and the Mission of Know'
Research Experience
  • Leads TZstats Convergence Lab to foster convergence of data science across disciplines
  • Participates in the NSF Science and Technology Center (STC) 'Learning the Earth with AI and Physics (LEAP)' to integrate climate science and data science for improved climate projections
  • Long-term collaboration with Professor Maria Uriarte on applying machine learning to study climate change impacts on tropical forests
  • Leads the 'AI for Social Good and Society (AI4SGS)' initiative applying AI to pressing social and public health challenges
  • Develops AutoClimDS project exploring generative AI tools to lower barriers in climate data science
  • Pioneers the 'Collaboratory' educational model using a crowdsourcing approach to create transdisciplinary data science pedagogy
  • Instructs 'Applied Data Science at Columbia', a project-based course launched in 2016 featuring multiple mini-projects per semester
Background
  • Professor of Statistics at Columbia University
  • Lead of TZstats Convergence Lab
  • Focuses on interdisciplinary collaboration to address real-world problems using data, tools, and methods
  • Committed to training the next generation of polymath researchers
  • Actively engaged in AI education, frontiers of statistics, and their applications in science and engineering