Statistical Learning and Data Science
ISSN: 3051-3901
Statistical Learning and Data Science
Advisory Board Members
David Donoho
Jianqing Fan
Michael I. Jordan
Jun Liu
Wing Hung Wong
Statistical Learning and Data Science (SLADS) is a peer‑reviewed journal dedicated to publishing cutting‑edge research and fostering discussions on future developments across all data‑driven disciplin...
Statistical Learning and Data Science (SLADS) is a peer‑reviewed journal dedicated to publishing cutting‑edge research and fostering discussions on future developments across all data‑driven disciplines, including but not limited to biology, economics, engineering, health sciences, humanities, physics, and social sciences.
SLADS serves the international statistics and data science community by publishing only the highest quality, field‑impacting research in statistics, machine learning, and data science. The journal is organized into three core sections:
1. Statistics
This section focuses on frontier research in statistical methodology and theory with close connections to machine learning and data science, as well as innovative studies of fundamental statistical problems arising from all scientific disciplines. We welcome contributions that advance theoretical understanding, develop new methodologies, or address emerging challenges in data‑driven research.
2. Machine Learning and Artificial Intelligence
This section is devoted to advances in the theory, methodology, and practice of machine learning and AI. We welcome contributions that develop novel algorithms, provide rigorous theoretical analysis, or establish new principles that deepen our understanding of learning from data. Topics of interest include large language models, deep learning, reinforcement learning, probabilistic generative models, optimization, privacy, interpretability, fairness, and the integration of statistical and computational perspectives. We particularly encourage papers that connect machine learning with statistical theory and methodology.
3. Data Science
This section focuses on the application of data science methods across a wide range of domains, including the natural and social sciences, health, medicine, engineering, economics, business, and the humanities. Contributions should be motivated by real‑world problems and leverage state‑of‑the‑art approaches in data science, machine learning, and artificial intelligence. We expect clear discussions of the problem, the datasets, the methodological choices, and the insights gained. We also publish resources—both data and tools—for data science research.
Society affiliation
Science China Press (https://www.scichina.com/) is owned by the Science Press -- one of the world's top STM publishers and publishes many high-level academic journals represented by Science China series, Science Bulletin, and National Science Review.
Call for Papers
Special Issue in Memory of Professor George C. Tiao
Submission deadline: August 31, 2027
Special Issue on Biomedical Data Science
Submission Deadline: June 30, 2026
Special Issue on High-Dimensional Data Modeling
Submission Deadline: September 30, 2026