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With the availability of data and information from multiple sources and domains, the past two decades have witnessed an explosive evolution on statistical methodologies for model transportability, generalizability, data exploitation, integration, and fusion. Concurrently, the machine learning community has also forged ahead with the creation of algorithms and approaches for transfer learning, out-of-distribution prediction, semi-supervised learning, and federated learning. While many research concepts and proposals share a pertinent and akin nature, they have not yet garnered unanimous recognition.

A primary objective of this workshop is to convene statisticians, biostatisticians, computer scientists, epidemiologists, social scientists, and industry researchers. The aim is twofold: firstly, to showcase the most recent advancements in this expansive realm of research; and secondly, to cast a visionary gaze towards new horizons that lie ahead.

Registration will open sometime in 2025.


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Chris Holmes Oxford University
Elizabeth Tipton Northwestern University
Jiwei Zhao University of Wisconsin, Madison