The Royal Statistical Society (RSS) is launching a new fully open access journal, RSS: Data Science and Artificial Intelligence. Created in recognition of the growing importance of data science and artificial intelligence in science and society, the new journal’s remit spans the breadth of data science; you can submit articles covering disciplines including statistics, machine learning, deep learning, econometrics, bioinformatics, engineering and computational social science.
As well as three primary paper types - method papers, applications papers and behind-the-scenes papers - RSS: Data Science and Artificial Intelligence will publish editorials, op-eds, interviews, and reviews/perspectives in line with its goal to become a primary destination for data scientists.
Published by Oxford University Press, this new journal is the first addition to the RSS family of world class statistics journals since 1952.
Learn more about why RSS: Data Science and Artificial Intelligence is the ideal platform for showcasing your research.
Meet the journal’s editors-in-chief and editorial board
Sach Mukherjee is Director of Research in Machine Learning for Biomedicine at the Medical Research Council (MRC) Biostatistics Unit, University of Cambridge, and Head of Statistics and Machine Learning at the German Center for Neurodegenerative Diseases.
Silvia Chiappa is a Research Scientist at Google DeepMind London, where she leads the Causal Intelligence team, and Honorary Professor at the Computer Science Department of University College London.
Neil Lawrenece is the inaugural DeepMind Professor of Machine Learning at the University of Cambridge. He has been working on machine learning models for over 20 years. He recently returned to academia after three years as Director of Machine Learning at Amazon.
View the full editorial board here: Editorial Board | RSS Data Science | Oxford Academic (oup.com)
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