Collection
Special Issue on Statistics for Data Science and AI
The development of statistical methods for data science is gaining more and more interest, not only for statisticians but also for computer scientists, computational mathematicians and physicists. The contamination among different scientific communities is becoming paramount to develop methodologies and approaches that can be beneficial to the advancement of data science methods. In particular, statistical methods can greatly contribute to make accurate, explainable, robust and fair (“trustworthy”) the most performing ML and AI algorithm. We hereby call for papers treating themes related to the modelling and analysis of complex data (structured, non-structured, mixed), using machine learning models, and at papers that propose novel approaches to measure the trustworthiness of such models, particularly in real applications . We encourage the submission of papers proposing cross-field methodologies which emphasize the multi-disciplinary trait.
Please see the Call for Papers for more information.
Editors
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Paola Cerchiello
University of Pavia, Italy
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Tomaso Aste
University College London, UK
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LIM Kian Guan
Singapore Management University, Singapore
Articles (8 in this collection)
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A probabilistic spatio-temporal neural network to forecast COVID-19 counts
Authors (first, second and last of 4)
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Establishing FAIR (Findable, Accessible, Interoperable and Reusable) principles for estuarine organisms exposed to engineered nanomaterials
Authors (first, second and last of 5)
