Terry Lyons, Lukasz Szpruch, Peter Foster and Renyuan Xu

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With increasing amount of high-dimensional time-series data there is a need for developing probabilistic methods which can help analyse such data, for example with respect to detecting anomalies or financial portfolios. Moreover, methods for generating synthetic data which reproduce main features of the original data are much in demand. The Wall Street Journal recently published a piece stating that “By 2024, 60% of the data used for the development of AI and analytics projects will be synthetically generated”. Approaches from Applied Probability may help develop and understand such generating methods.

This workshop will bring together researchers from the field of applied probability and data science. The first two talks will be live talks. The second two presentations will discuss two pre-recorded talks. The workshop will conclude with an overarching discussion.