Faculty leaders from 35 statistics and data science programs met March 20 at Washington University in St. Louis to identify emerging challenges, share best practices, and formulate action plans for graduate education. Its unifying diagnosis: The discipline’s problem is not relevance, but positioning, and the response must be proactive, outward-facing, and ambitious.
Five plenary talks framed the discussion. Kate Calder made the case for cultivating “ambition,” outlining five pillars for building an ambitious culture in academia. Xiao-Li Meng urged statisticians to shed their reputations as naysayers and embrace data science and AI as an ecosystem organized around the data life cycle. Galin Jones described building resilience at the University of Minnesota through broader engagement with internal and external partners, and he insisted statistics assert itself as a foundational discipline of AI. Kimberly Sellers pointed to recent program eliminations at major universities and called for modernizing curricula and shifting the narrative from defensive to proactive. Abel Rodriguez examined how large language models are reshaping teaching, assessment, and funding, arguing that graduate programs must slim their core requirements to allow flexibility and statisticians should lead interdisciplinary teams rather than merely collaborate.
Three breakout groups took up AI integration, pipeline and recruitment, and communication and leadership.
The workshop was organized by the department of statistics and data science at WashU and co-sponsored by the American Statistical Association.
Read the full report in ASA Discoveries.
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