TZStats aiX Lab @ Columbia
Where disciplines collaborate and research meets education: tackling real-world problems with data, tools, methods — and preparing the next generation of polymath researchers.
Latest Posts
AI is making some old pressures in research louder: more proposals, more submissions, more reviews. That’s a good prompt for a simple question. What problem do we actually want AI to solve, and how will we know it worked?
Many debates about AI turn into “contests” with human: can AI grade, tutor, or advise as well as a person? This issue aims to think about AI in our work from a different premise. AI governance is not about proving whether humans or AI are better at something. It is about how we choose to use technology in our lives.
An FAQ bot I built for a student poster session taught me how an AI that only wants to be helpful can end up suggesting something unethical.
Almost every institution now agrees that decisions and actions about AI in Education belongs inside the disciplines. The harder question is what each discipline needs from it, and what each needs to protect from it.
Featured Projects
AI for Social Good and Society
The AI for Social Good and Society (AI4SGS) Initiative is a bold interdisciplinary effort to apply artificial intelligence to some of the world’s most pressing social and public health challenges.
AutoClimDS: Climate Data Science Agentic AI
We explore genAI tools to lower the barriers in Climate Data Science in collaboration with AWS.
Currect subprojects include: Knowledge graph construction and expansion. Development of agents for data acquision, analysis, modeling, visualization, etc. Evaluation through case studies.
NSF-STC: Learning the Earth with AI and Physics
Learning the Earth with Artificial Intelligence and Physics (LEAP) is an NSF Science and Technology Center (STC) launched in 2021. LEAP’s mission is to increase the reliability, utility, and reach of climate projections through the integration of climate and data science.
Statistical Machine Learning for Ecology
A long-time collaboration between Professors Tian Zheng and Professor Maria Uriarte on using machine learning to unlock potentials of new data types to understand the impact of climate change on tropical forests.
Collaboratory at Columbia - An Aspen Grove of Data Science Education
The Collaboratory is both a set of “data science in context” educational approaches, as well as a meta-model for an accelerator program that allows different institutions to respond flexibly to their own disciplinary heterogeneity in terms of data science educational needs. The novelty of the Collaboratory lies in its crowd-sourcing approach to creating new data science pedagogy and its ability to kindle transdisciplinary collaboration in doing so. Read our Havard Data Science Review article to learn more.
Applied Data Science at Columbia
Applied Data Science at Columbia is a project-based learning course that started in 2016. It employs the common task framework and runs 5 mini project cycles during one semester to give students a broad exposure to various areas in data science. Projects are developed and updated each year, drawing inspirations from active research, challenges and interesting public datasets.
Statistical Methods for Aggregated Relational Data
A core and active research area of the TZstats lab.