Olga Korosteleva seeks mentors to guide high school students through intensive, yearlong projects in statistics and machine learning.
artificial intellegence
Service, Leadership, and Innovation: An Interview with 2027 ASA President Brian Millen
Millen shares his journey of leadership, service, and innovation in statistics, highlighting his vision for advocacy, mentorship, diversity, and the future of the profession in the era of AI and data science.
Statistics, Data Science, and AI Enriching Society: Insights from JSM 2025
The conference showcased the central role of statistics in shaping the age of AI and featured high-profile lectures; timely sessions on causal inference, data privacy, and omics research; and memorials honoring leaders in the field.
Looking Ahead to JSM 2025: A Time to Reconnect, Reflect, and Rebuild
ASA President Ji-Hyun Lee invites statisticians to attend JSM 2025 in Nashville to reconnect with the community, share research on the theme “Statistics, Data Science, and AI: Enriching Society,” and support one another during challenging times.
SDSS 2025 Explores Advances in AI, Statistics, Data Science
The 2025 Symposium on Data Science and Statistics in Salt Lake City brought together more than 350 data scientists, statisticians, and computer scientists to explore interdisciplinary advances in AI, statistics, and data science.
NSF Institutes Develop AI for Public Benefit
In his Stats4Good column, David Corliss discusses the NSF’s 25 national artificial intelligence research institutes to create a cohesive approach to AI-related opportunities and risks.
Judea Pearl, AI, and Causality: What Role Do Statisticians Play?
In the first half of 2023, the machine learning programs ChatGPT and GPT-4 changed the landscape of artificial intelligence research seemingly overnight. Judea Pearl’s research bridges the subjects of statistics and artificial intelligence and highlights the importance of causality in both settings. Dana Mackenzie, Pearl’s co-author for The Book of Why, interviews him here to get his take on recent developments. When they wrote their book in 2018, Pearl contended machine learning had not yet moved past the first rung of the “ladder of causation.” Computers could not correctly answer queries about interventions and still less about counterfactual scenarios. Has his assessment changed?