SLADS

Statistics in the 2nd Quarter of the 21st Century

Guest Editors: Song Xi Chen (Tsinghua University), Jiashun Jin (Carnegie Mellon University)

Recently, both in academic meetings and on social media, we have seen widespread discussions about the future of statistics, especially regarding its relationship with AI. Many researchers feel confused about the discipline’s direction, with some calling for reforms in statistical journals, teaching, and practice. Motivated by these, SLADS decides to organize a special issue. The special issue aims to publish a handful of articles, where the authors may share their perspectives on (1) Statistics (research and education) in the next decades: challenges and opportunities and (2) Statistics and AI: synergy and competitions. While the intersection of statistics and AI is an exciting avenue, we equally welcome perspectives on the broader future of statistics: its foundational principles, new methodological frontiers, its role in scientific and societal contexts, and other directions you consider significant.

Special issue on Statistics and AI

Guest Editors: Xiaowu Dai (University of California, Los Angeles), Linglong Kong (University of Alberta), Weijie Su (University of Pennsylvania), Zhihua Zhang (Peking University)

This special issue aims to offer a venue for publishing high-impact statistical work in the theory, methodology, and applications at the frontier of AI. We seek to highlight research that either (1) applies statistical methodology to understand, improve, and validate AI systems, or (2) develops novel AI-driven approaches to solve complex statistical and data science problems. We are particularly interested in submissions that bridge the gap between theory and practice and address the reliability and efficiency of AI from a statistical perspective.

Special Issue on Frontiers in Statistical Learning: Data, Networks, and Knowledge Transfer

Guest Editors: Feng Yang (New York University), Guangming Pan (Nanyang Technological University), Jiaming Xu (Duke University), Emma Zhang (Emory University)

This special issue aims to showcase cutting-edge advances in theory, methodology, and applications across the broad spectrum of modern statistical learning.

Mathematical Statistics of Machine Learning

Guest Editors: Cristina Bubucea (CREST, ENSAE, IP PARIS), Edgar Dobriban (University Pennsylvania), Anru Zhang (Duke University)

Special issue on Data Science

Guest Editors: Hongyu Zhao (Yale University)

Special issue on High-Dimensional Statistics

Guest Editors: Runze Li (Pennsylvania State University)