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